diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/.gitattributes b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/.gitattributes
new file mode 100644
index 0000000..a6344aa
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/README.md b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/README.md
new file mode 100644
index 0000000..6f431de
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/README.md
@@ -0,0 +1,29 @@
+---
+library_name: diffusers
+pipeline_tag: image-to-image
+license: apache-2.0
+---
+# Model Card for StableNormal
+
+This repository contains the weights of StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
+
+## Usage
+
+See the Github repository: https://github.com/Stable-X/StableNormal regarding installation instructions.
+
+The model can then be used as follows:
+
+```python
+import torch
+from PIL import Image
+# Load an image
+input_image = Image.open("path/to/your/image.jpg")
+# Create predictor instance
+predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal_turbo", trust_repo=True, yoso_version='yoso-normal-v1-8-1')
+# Generate normal map using alpha channel for masking
+normal_map = predictor(rgba_image, data_type="object")  # Will mask out background, if alpha channel is avalible, else use birefnet
+# Apply the model to the image
+normal_image = predictor(input_image)
+# Save or display the result
+normal_image.save("output/normal_map.png")
+```
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/controlnet/config.json b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/controlnet/config.json
new file mode 100644
index 0000000..8ee2bb4
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/controlnet/config.json
@@ -0,0 +1,57 @@
+{
+  "_class_name": "ControlNetVAEModel",
+  "_diffusers_version": "0.28.0",
+  "_name_or_path": "weights/yoso-normal-v1-8-1/controlnet",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_channels": 3,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 1024,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": 1,
+  "upcast_attention": true,
+  "use_linear_projection": true
+}
diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/controlnet/controlnetvae.py b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/controlnet/controlnetvae.py
new file mode 100644
index 0000000..cc1c520
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/controlnet/controlnetvae.py
@@ -0,0 +1,250 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders.single_file_model import FromOriginalModelMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unets.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    DownBlock2D,
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+)
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models.controlnet import ControlNetOutput
+from diffusers.models import ControlNetModel
+
+import pdb
+
+
+class ControlNetVAEModel(ControlNetModel):
+    def forward(
+        self,
+        sample: torch.Tensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        controlnet_cond: torch.Tensor = None,
+        conditioning_scale: float = 1.0,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guess_mode: bool = False,
+        return_dict: bool = True,
+    ) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
+        """
+        The [`ControlNetVAEModel`] forward method.
+
+        Args:
+            sample (`torch.Tensor`):
+                The noisy input tensor.
+            timestep (`Union[torch.Tensor, float, int]`):
+                The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.Tensor`):
+                The encoder hidden states.
+            controlnet_cond (`torch.Tensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
+                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
+                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
+                embeddings.
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            added_cond_kwargs (`dict`):
+                Additional conditions for the Stable Diffusion XL UNet.
+            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
+                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
+            guess_mode (`bool`, defaults to `False`):
+                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
+                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
+            return_dict (`bool`, defaults to `True`):
+                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
+                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
+                returned where the first element is the sample tensor.
+        """
+        # check channel order
+
+
+        channel_order = self.config.controlnet_conditioning_channel_order
+
+        if channel_order == "rgb":
+            # in rgb order by default
+            ...
+        elif channel_order == "bgr":
+            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
+        else:
+            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        if self.config.addition_embed_type is not None:
+            if self.config.addition_embed_type == "text":
+                aug_emb = self.add_embedding(encoder_hidden_states)
+
+            elif self.config.addition_embed_type == "text_time":
+                if "text_embeds" not in added_cond_kwargs:
+                    raise ValueError(
+                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                    )
+                text_embeds = added_cond_kwargs.get("text_embeds")
+                if "time_ids" not in added_cond_kwargs:
+                    raise ValueError(
+                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                    )
+                time_ids = added_cond_kwargs.get("time_ids")
+                time_embeds = self.add_time_proj(time_ids.flatten())
+                time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+                add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+                add_embeds = add_embeds.to(emb.dtype)
+                aug_emb = self.add_embedding(add_embeds)
+
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
+                sample = self.mid_block(
+                    sample,
+                    emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+            else:
+                sample = self.mid_block(sample, emb)
+
+        # 5. Control net blocks
+
+        controlnet_down_block_res_samples = ()
+
+        # NOTE that controlnet downblock is zeroconv, we discard
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+            down_block_res_sample = down_block_res_sample
+            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
+
+        down_block_res_samples = controlnet_down_block_res_samples
+
+        mid_block_res_sample = sample
+
+        # 6. scaling
+        if guess_mode and not self.config.global_pool_conditions:
+            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
+            scales = scales * conditioning_scale
+            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
+            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
+        else:
+            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
+            mid_block_res_sample = mid_block_res_sample * conditioning_scale
+
+        if self.config.global_pool_conditions:
+            down_block_res_samples = [
+                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
+            ]
+            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
+
+        if not return_dict:
+            return (down_block_res_samples, mid_block_res_sample)
+
+        return ControlNetOutput(
+            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
+        )
+
+
+
diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/model_index.json b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/model_index.json
new file mode 100644
index 0000000..43061ae
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/model_index.json
@@ -0,0 +1,17 @@
+{
+  "_class_name": "YOSONormalsPipeline",
+  "_diffusers_version": "0.28.0",
+  "controlnet": [
+    "controlnetvae",
+    "ControlNetVAEModel"
+  ],
+  "requires_safety_checker": true,
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/unet/config.json b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/unet/config.json
new file mode 100644
index 0000000..7dc67b0
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/unet/config.json
@@ -0,0 +1,73 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.28.0",
+  "_name_or_path": "weights/yoso-normal-v1-8-1/unet",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 1024,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 128,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": true,
+  "use_linear_projection": true
+}
diff --git a/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/vae/config.json b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/vae/config.json
new file mode 100644
index 0000000..b7e29a0
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/stablex/yoso-normal-v1-8-1/vae/config.json
@@ -0,0 +1,34 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.28.0",
+  "_name_or_path": "weights/yoso-normal-v1-8-1/vae",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/.gitattributes b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/.gitattributes
new file mode 100644
index 0000000..a6344aa
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/README.md b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/README.md
new file mode 100644
index 0000000..3c96cc0
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/README.md
@@ -0,0 +1,9 @@
+---
+library_name: trellis
+pipeline_tag: image-to-3d
+license: mit
+language:
+- en
+---
+
+An improved normal conditioned version of TRELLIS.
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.json b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.json
new file mode 100644
index 0000000..2880282
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.json
@@ -0,0 +1,17 @@
+{
+    "name": "SLatMeshDecoder",
+    "args": {
+        "resolution": 64,
+        "model_channels": 768,
+        "latent_channels": 8,
+        "num_blocks": 12,
+        "num_heads": 12,
+        "mlp_ratio": 4,
+        "attn_mode": "swin",
+        "window_size": 8,
+        "use_fp16": true,
+        "representation_config": {
+            "use_color": true
+        }
+    }
+}
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/slat_flow_normal_dit_L_64l8p2_fp16.json b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/slat_flow_normal_dit_L_64l8p2_fp16.json
new file mode 100644
index 0000000..22f500a
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/slat_flow_normal_dit_L_64l8p2_fp16.json
@@ -0,0 +1,19 @@
+{
+    "name": "SLatFlowModel",
+    "args": {
+        "resolution": 64,
+        "in_channels": 8,
+        "out_channels": 8,
+        "model_channels": 1024,
+        "cond_channels": 1024,
+        "num_blocks": 24,
+        "num_heads": 16,
+        "mlp_ratio": 4,
+        "patch_size": 2,
+        "num_io_res_blocks": 2,
+        "io_block_channels": [128],
+        "pe_mode": "ape",
+        "qk_rms_norm": true,
+        "use_fp16": true
+    }
+}
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/ss_dec_conv3d_16l8_fp16.json b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/ss_dec_conv3d_16l8_fp16.json
new file mode 100644
index 0000000..9f3affa
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/ss_dec_conv3d_16l8_fp16.json
@@ -0,0 +1,12 @@
+
+{
+    "name": "SparseStructureDecoder",
+    "args": {
+        "out_channels": 1,
+        "latent_channels": 8,
+        "num_res_blocks": 2,
+        "num_res_blocks_middle": 2,
+        "channels": [512, 128, 32],
+        "use_fp16": true
+    }
+}
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/ss_flow_normal_dit_L_16l8_fp16.json b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/ss_flow_normal_dit_L_16l8_fp16.json
new file mode 100644
index 0000000..d228f99
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/ckpts/ss_flow_normal_dit_L_16l8_fp16.json
@@ -0,0 +1,17 @@
+{
+    "name": "SparseStructureFlowModel",
+    "args": {
+        "resolution": 16,
+        "in_channels": 8,
+        "out_channels": 8,
+        "model_channels": 1024,
+        "cond_channels": 1024,
+        "num_blocks": 24,
+        "num_heads": 16,
+        "mlp_ratio": 4,
+        "patch_size": 1,
+        "pe_mode": "ape",
+        "qk_rms_norm": true,
+        "use_fp16": true
+    }
+}
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/pipeline.json b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/pipeline.json
new file mode 100644
index 0000000..2f41162
--- /dev/null
+++ b/Checkpoints/Diffusers/Stable3DGen/trellis/trellis-normal-v0-1/pipeline.json
@@ -0,0 +1,58 @@
+{
+    "name": "TrellisImageTo3DPipeline",
+    "args": {
+        "models": {
+            "sparse_structure_decoder": "ckpts/ss_dec_conv3d_16l8_fp16",
+            "sparse_structure_flow_model": "ckpts/ss_flow_normal_dit_L_16l8_fp16",
+            "slat_decoder_mesh": "ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16",
+            "slat_flow_model": "ckpts/slat_flow_normal_dit_L_64l8p2_fp16"
+        },
+        "sparse_structure_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 25,
+                "cfg_strength": 5.0,
+                "cfg_interval": [0.5, 1.0],
+                "rescale_t": 3.0
+            }
+        },
+        "slat_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 25,
+                "cfg_strength": 5.0,
+                "cfg_interval": [0.5, 1.0],
+                "rescale_t": 3.0
+            }
+        },
+        "slat_normalization": {
+            "mean": [
+                -2.1687545776367188,
+                -0.004347046371549368,
+                -0.13352349400520325,
+                -0.08418072760105133,
+                -0.5271206498146057,
+                0.7238689064979553,
+                -1.1414450407028198,
+                1.2039363384246826
+            ],
+            "std": [
+                2.377650737762451,
+                2.386378288269043,
+                2.124418020248413,
+                2.1748552322387695,
+                2.663944721221924,
+                2.371192216873169,
+                2.6217446327209473,
+                2.684523105621338
+            ]
+        },
+        "image_cond_model": "dinov2_vitl14_reg"
+    }
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes
new file mode 100644
index 0000000..a6344aa
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md
new file mode 100644
index 0000000..58a903f
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/README.md
@@ -0,0 +1,49 @@
+---
+license: mit
+pipeline_tag: image-to-3d
+tags:
+- triposg
+- 3d-generation
+- rectified-flow
+---
+# TripoSG-scribble - Fast 3D Shape Prototyping with Scribble and Prompt
+
+TripoSG-scribble converts a scribble image and a text prompt to a 3D shape. TripoSG-scribble is a variant of TripoSG. TripoSG is a state-of-the-art image-to-3D generation foundation model that leverages large-scale rectified flow transformers to produce high-fidelity 3D shapes from single images.
+
+## Model Description
+
+### Model Architecture
+
+TripoSG utilizes a novel architecture combining:
+- Rectified Flow (RF) based Transformer for stable, linear trajectory modeling
+- Advanced VAE with SDF-based representation and hybrid geometric supervision
+- Cross-attention mechanism for image feature condition
+- 1.5B parameters operating on 2048 latent tokens
+
+For inference efficiency, TripoSG-scribble is different from TripoSG in:
+- TripoSG-scribble is a CFG-distilled model and should be used with CFG=0
+- TripoSG-scribble is trained with 512 latent tokens
+
+## Intended Uses
+
+This model is designed for:
+- Converting scribble image and text prompt to high-quality 3D meshes
+- Creative and design applications
+- Gaming and VFX asset creation
+- Prototyping and visualization
+
+## Requirements
+
+- CUDA-capable GPU (>8GB VRAM)
+
+## Usage
+
+For detailed usage instructions, please visit our [GitHub repository](https://github.com/VAST-AI-Research/TripoSG).
+
+## About
+
+TripoSG-scribble is developed by [Tripo](https://www.tripo3d.ai), [VAST AI Research](https://github.com/orgs/VAST-AI-Research), pushing the boundaries of 3D Generative AI.
+For more information:
+- [GitHub Repository](https://github.com/VAST-AI-Research/TripoSG)
+- [Paper](https://arxiv.org/abs/2502.06608)
+- [Gradio Demo](https://huggingface.co/spaces/VAST-AI/TripoSG-scribble)
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json
new file mode 100644
index 0000000..ff5b47c
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/feature_extractor_dinov2/preprocessor_config.json
@@ -0,0 +1,27 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "BitImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 256
+  }
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json
new file mode 100644
index 0000000..0e2c2ac
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/image_encoder_dinov2/config.json
@@ -0,0 +1,60 @@
+{
+  "apply_layernorm": true,
+  "architectures": [
+    "Dinov2Model"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "drop_path_rate": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "image_size": 518,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-06,
+  "layerscale_value": 1.0,
+  "mlp_ratio": 4,
+  "model_type": "dinov2",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "out_features": [
+    "stage24"
+  ],
+  "out_indices": [
+    24
+  ],
+  "patch_size": 14,
+  "qkv_bias": true,
+  "reshape_hidden_states": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4",
+    "stage5",
+    "stage6",
+    "stage7",
+    "stage8",
+    "stage9",
+    "stage10",
+    "stage11",
+    "stage12",
+    "stage13",
+    "stage14",
+    "stage15",
+    "stage16",
+    "stage17",
+    "stage18",
+    "stage19",
+    "stage20",
+    "stage21",
+    "stage22",
+    "stage23",
+    "stage24"
+  ],
+  "torch_dtype": "float16",
+  "transformers_version": "4.50.3",
+  "use_mask_token": true,
+  "use_swiglu_ffn": false
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json
new file mode 100644
index 0000000..f80ab2f
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/model_index.json
@@ -0,0 +1,32 @@
+{
+  "_class_name": "TripoSGScribblePipeline",
+  "_diffusers_version": "0.33.1",
+  "feature_extractor_dinov2": [
+    "transformers",
+    "BitImageProcessor"
+  ],
+  "image_encoder_dinov2": [
+    "transformers",
+    "Dinov2Model"
+  ],
+  "scheduler": [
+    "TripoSG.schedulers.scheduling_rectified_flow",
+    "RectifiedFlowScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "transformer": [
+    "TripoSG.models.transformers.triposg_transformer",
+    "TripoSGDiTModel"
+  ],
+  "vae": [
+    "TripoSG.models.autoencoders.autoencoder_kl_triposg",
+    "TripoSGVAEModel"
+  ]
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json
new file mode 100644
index 0000000..8bc9f2b
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/scheduler/scheduler_config.json
@@ -0,0 +1,7 @@
+{
+  "_class_name": "RectifiedFlowScheduler",
+  "_diffusers_version": "0.33.1",
+  "num_train_timesteps": 1000,
+  "shift": 1,
+  "use_dynamic_shifting": false
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json
new file mode 100644
index 0000000..8f7a43f
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/text_encoder/config.json
@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.50.3",
+  "vocab_size": 49408
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
+i n
+t h
+a n
+r e
+a r
+e r
+th e</w>
+in g</w>
+o u
+o n
+s t
+o r
+e n
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+a l
+a t
+e r</w>
+i t
+i n</w>
+t o</w>
+r o
+i s</w>
+l e
+i c
+a t</w>
+an d</w>
+e d</w>
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+c h
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+e s</w>
+i l
+e l
+s t</w>
+a c
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+a m
+l o
+a n</w>
+a y</w>
+s h
+r i
+l i
+t i
+f or</w>
+n e
+ð Ł
+r a
+h a
+d e
+o l
+v e</w>
+s i
+u r
+a l</w>
+s e
+' s</w>
+u n
+d i
+b e
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+o o
+d ay</w>
+e n</w>
+m a
+n o
+l e</w>
+t o
+ou r</w>
+i r
+g h
+w it
+i t</w>
+y o
+a s
+s p
+th is</w>
+t s</w>
+at i
+yo u</w>
+wit h</w>
+a d
+i s
+a b
+l y</w>
+w e
+th e
+t e
+a s</w>
+a g
+v i
+p p
+s u
+h o
+m y</w>
+. .
+b u
+c om
+s e</w>
+er s</w>
+m e
+m e</w>
+al l</w>
+c on
+m o
+k e</w>
+g e
+ou t</w>
+en t</w>
+c o
+f e
+v er
+a r</w>
+f ro
+a u
+p o
+c e</w>
+gh t</w>
+ar e</w>
+s s</w>
+fro m</w>
+c h</w>
+t r
+ou n
+on e</w>
+b y</w>
+d o
+t h</w>
+w or
+er e</w>
+k e
+p ro
+f or
+d s</w>
+b o
+t a
+w e</w>
+g o
+h e
+t er</w>
+in g
+d e</w>
+b e</w>
+ati on</w>
+m or
+a y
+e x
+il l</w>
+p e
+k s</w>
+s c
+l u
+f u
+q u
+v er</w>
+ðŁ ĺ
+j u
+m u
+at e</w>
+an d
+v e
+k ing</w>
+m ar
+o p
+h i
+.. .</w>
+p re
+a d</w>
+r u
+th at</w>
+j o
+o f
+c e
+ne w</w>
+a m</w>
+a p
+g re
+s s
+d u
+no w</w>
+y e
+t ing</w>
+y our</w>
+it y</w>
+n i
+c i
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new file mode 100644
index 0000000..b5018cd
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/tokenizer_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..0ecd08d
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/tokenizer_config.json
@@ -0,0 +1,31 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/vocab.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/vocab.json
new file mode 100644
index 0000000..469be27
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/tokenizer/vocab.json
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diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json
new file mode 100644
index 0000000..bd1e3ba
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/transformer/config.json
@@ -0,0 +1,11 @@
+{
+  "_class_name": "TripoSGDiTModel",
+  "_diffusers_version": "0.33.1",
+  "cross_attention_2_dim": 1024,
+  "cross_attention_dim": 768,
+  "in_channels": 64,
+  "num_attention_heads": 16,
+  "num_layers": 21,
+  "use_cross_attention_2": true,
+  "width": 2048
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json
new file mode 100644
index 0000000..b35bce7
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG-scribble/vae/config.json
@@ -0,0 +1,14 @@
+{
+  "_class_name": "TripoSGVAEModel",
+  "_diffusers_version": "0.33.1",
+  "embed_frequency": 8,
+  "embed_include_pi": false,
+  "embedding_type": "frequency",
+  "in_channels": 3,
+  "latent_channels": 64,
+  "num_attention_heads": 8,
+  "num_layers_decoder": 16,
+  "num_layers_encoder": 8,
+  "width_decoder": 1024,
+  "width_encoder": 512
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/.gitattributes b/Checkpoints/Diffusers/VAST-AI/TripoSG/.gitattributes
new file mode 100644
index 0000000..b2fd9ee
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/.gitattributes
@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/README.md b/Checkpoints/Diffusers/VAST-AI/TripoSG/README.md
new file mode 100644
index 0000000..d48215b
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/README.md
@@ -0,0 +1,45 @@
+---
+license: mit
+pipeline_tag: image-to-3d
+tags:
+- triposg
+- 3d-generation
+- rectified-flow
+---
+# TripoSG - High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
+
+TripoSG is a state-of-the-art image-to-3D generation foundation model that leverages large-scale rectified flow transformers to produce high-fidelity 3D shapes from single images.
+
+## Model Description
+
+### Model Architecture
+
+TripoSG utilizes a novel architecture combining:
+- Rectified Flow (RF) based Transformer for stable, linear trajectory modeling
+- Advanced VAE with SDF-based representation and hybrid geometric supervision
+- Cross-attention mechanism for image feature condition
+- 1.5B parameters operating on 2048 latent tokens
+
+## Intended Uses
+
+This model is designed for:
+- Converting single images to high-quality 3D meshes
+- Creative and design applications
+- Gaming and VFX asset creation
+- Prototyping and visualization
+
+## Requirements
+
+- CUDA-capable GPU (>8GB VRAM)
+
+## Usage
+
+For detailed usage instructions, please visit our [GitHub repository](https://github.com/VAST-AI-Research/TripoSG).
+
+## About
+
+TripoSG is developed by [Tripo](https://www.tripo3d.ai), [VAST AI Research](https://github.com/orgs/VAST-AI-Research), pushing the boundaries of 3D Generative AI.
+For more information:
+- [GitHub Repository](https://github.com/VAST-AI-Research/TripoSG)
+- [Paper](https://arxiv.org/abs/2502.06608)
+- [Gradio Demo](https://huggingface.co/spaces/VAST-AI/TripoSG)
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json
new file mode 100644
index 0000000..ff5b47c
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/feature_extractor_dinov2/preprocessor_config.json
@@ -0,0 +1,27 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "BitImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 256
+  }
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json
new file mode 100644
index 0000000..c3f240c
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/image_encoder_dinov2/config.json
@@ -0,0 +1,60 @@
+{
+  "_name_or_path": "facebook/dinov2-large",
+  "apply_layernorm": true,
+  "architectures": [
+    "Dinov2Model"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "drop_path_rate": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "image_size": 518,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-06,
+  "layerscale_value": 1.0,
+  "mlp_ratio": 4,
+  "model_type": "dinov2",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "out_features": [
+    "stage24"
+  ],
+  "out_indices": [
+    24
+  ],
+  "patch_size": 14,
+  "qkv_bias": true,
+  "reshape_hidden_states": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4",
+    "stage5",
+    "stage6",
+    "stage7",
+    "stage8",
+    "stage9",
+    "stage10",
+    "stage11",
+    "stage12",
+    "stage13",
+    "stage14",
+    "stage15",
+    "stage16",
+    "stage17",
+    "stage18",
+    "stage19",
+    "stage20",
+    "stage21",
+    "stage22",
+    "stage23",
+    "stage24"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "use_swiglu_ffn": false
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json
new file mode 100644
index 0000000..5d01bbd
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/model_index.json
@@ -0,0 +1,24 @@
+{
+  "_class_name": "TripoSGPipeline",
+  "_diffusers_version": "0.30.3",
+  "feature_extractor_dinov2": [
+    "transformers",
+    "BitImageProcessor"
+  ],
+  "image_encoder_dinov2": [
+    "transformers",
+    "Dinov2Model"
+  ],
+  "scheduler": [
+    "TripoSG.schedulers.scheduling_rectified_flow",
+    "RectifiedFlowScheduler"
+  ],
+  "transformer": [
+    "TripoSG.models.transformers.triposg_transformer",
+    "TripoSGDiTModel"
+  ],
+  "vae": [
+    "TripoSG.models.autoencoders.autoencoder_kl_triposg",
+    "TripoSGVAEModel"
+  ]
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json
new file mode 100644
index 0000000..da0fa53
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/scheduler/scheduler_config.json
@@ -0,0 +1,7 @@
+{
+  "_class_name": "RectifiedFlowScheduler",
+  "_diffusers_version": "0.30.3",
+  "num_train_timesteps": 1000,
+  "shift": 1,
+  "use_dynamic_shifting": false
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json
new file mode 100644
index 0000000..a21d98d
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/transformer/config.json
@@ -0,0 +1,9 @@
+{
+  "_class_name": "TripoSGDiTModel",
+  "_diffusers_version": "0.30.3",
+  "cross_attention_dim": 1024,
+  "in_channels": 64,
+  "num_attention_heads": 16,
+  "num_layers": 21,
+  "width": 2048
+}
diff --git a/Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json b/Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json
new file mode 100644
index 0000000..c5d72c0
--- /dev/null
+++ b/Checkpoints/Diffusers/VAST-AI/TripoSG/vae/config.json
@@ -0,0 +1,14 @@
+{
+  "_class_name": "Tripo2VAEModel",
+  "_diffusers_version": "0.30.3",
+  "embed_frequency": 8,
+  "embed_include_pi": false,
+  "embedding_type": "frequency",
+  "in_channels": 3,
+  "latent_channels": 64,
+  "num_attention_heads": 8,
+  "num_layers_decoder": 16,
+  "num_layers_encoder": 8,
+  "width_decoder": 1024,
+  "width_encoder": 512
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/README.md b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/README.md
deleted file mode 100644
index 51ebc3e..0000000
--- a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/README.md
+++ /dev/null
@@ -1,224 +0,0 @@
----
-library_name: hunyuan3d-2.0
-license: other
-license_name: tencent-hunyuan-community
-license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt
-language:
-  - en
-  - zh
-tags:
-  - image-to-3d
-  - text-to-3d
-pipeline_tag: image-to-3d
----
-
-<p align="center">
-  <img src="./assets/images/teaser.jpg">
-</p>
-
-<div align="center">
-  <a href=https://3d.hunyuan.tencent.com target="_blank"><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a>
-  <a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2  target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a>
-  <a href=https://huggingface.co/tencent/Hunyuan3D-2 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
-  <a href=https://3d-models.hunyuan.tencent.com/ target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a>
-<a href=https://discord.gg/GuaWYwzKbX target="_blank"><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a>
-    <a href=https://github.com/Tencent/Hunyuan3D-2/blob/main/assets/report/Tencent_Hunyuan3D_2_0.pdf target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
-</div>
-
-
-[//]: # (  <a href=# target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>)
-
-[//]: # (  <a href=# target="_blank"><img src= https://img.shields.io/badge/Colab-8f2628.svg?logo=googlecolab height=22px></a>)
-
-[//]: # (  <a href="#"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/v/mulankit?logo=pypi"  height=22px></a>)
-
-<br>
-<p align="center">
-“ Living out everyone’s imagination on creating and manipulating 3D assets.”
-</p>
-
-This repository contains the models of the paper [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://huggingface.co/papers/2501.12202).
-For code and more details on how to use it, refer to the [Github repository](https://github.com/Tencent/Hunyuan3D-2).
-
-## 🔥 News
-
-- Jan 21, 2025: 💬 Release [Hunyuan3D 2.0](https://huggingface.co/spaces/tencent/Hunyuan3D-2). Please give it a try!
-
-## **Abstract**
-
-We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets.
-This system includes two foundation components: a large-scale shape generation model - Hunyuan3D-DiT, and a large-scale
-texture synthesis model - Hunyuan3D-Paint.
-The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly
-aligns with a given condition image, laying a solid foundation for downstream applications.
-The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant
-texture maps for either generated or hand-crafted meshes.
-Furthermore, we build Hunyuan3D-Studio - a versatile, user-friendly production platform that simplifies the re-creation
-process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes
-efficiently.
-We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models,
-including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and
-e.t.c.
-
-<p align="center">
-  <img src="assets/images/system.jpg">
-</p>
-
-## ☯️ **Hunyuan3D 2.0**
-
-### Architecture
-
-Hunyuan3D 2.0 features a two-stage generation pipeline, starting with the creation of a bare mesh, followed by the
-synthesis of a texture map for that mesh. This strategy is effective for decoupling the difficulties of shape and
-texture generation and also provides flexibility for texturing either generated or handcrafted meshes.
-
-<p align="left">
-  <img src="assets/images/arch.jpg">
-</p>
-
-### Performance
-
-We have evaluated Hunyuan3D 2.0 with other open-source as well as close-source 3d-generation methods.
-The numerical results indicate that Hunyuan3D 2.0 surpasses all baselines in the quality of generated textured 3D assets
-and the condition following ability.
-
-| Model                   | CMMD(⬇)   | FID_CLIP(⬇) | FID(⬇)      | CLIP-score(⬆) |
-|-------------------------|-----------|-------------|-------------|---------------|
-| Top Open-source Model1  | 3.591     | 54.639      | 289.287     | 0.787         |
-| Top Close-source Model1 | 3.600     | 55.866      | 305.922     | 0.779         |
-| Top Close-source Model2 | 3.368     | 49.744      | 294.628     | 0.806         |
-| Top Close-source Model3 | 3.218     | 51.574      | 295.691     | 0.799         |
-| Hunyuan3D 2.0           | **3.193** | **49.165**  | **282.429** | **0.809**     |
-
-Generation results of Hunyuan3D 2.0:
-<p align="left">
-  <img src="assets/images/e2e-1.gif"  height=300>
-  <img src="assets/images/e2e-2.gif"  height=300>
-</p>
-
-### Pretrained Models
-
-| Model                | Date       | Huggingface                                            |
-|----------------------|------------|--------------------------------------------------------| 
-| Hunyuan3D-DiT-v2-0   | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) |
-| Hunyuan3D-Paint-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) |
-| Hunyuan3D-Delight-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-delight-v2-0) |
-
-## 🤗 Get Started with Hunyuan3D 2.0
-
-You may follow the next steps to use Hunyuan3D 2.0 via code or the Gradio App.
-
-### Install Requirements
-
-Please install Pytorch via the [official](https://pytorch.org/) site. Then install the other requirements via
-
-```bash
-pip install -r requirements.txt
-# for texture
-cd hy3dgen/texgen/custom_rasterizer
-python3 setup.py install
-cd ../../..
-cd hy3dgen/texgen/differentiable_renderer
-bash compile_mesh_painter.sh OR python3 setup.py install (on Windows)
-```
-
-### API Usage
-
-We designed a diffusers-like API to use our shape generation model - Hunyuan3D-DiT and texture synthesis model -
-Hunyuan3D-Paint.
-
-You could assess **Hunyuan3D-DiT** via:
-
-```python
-from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
-
-pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
-mesh = pipeline(image='assets/demo.png')[0]
-```
-
-The output mesh is a [trimesh object](https://trimesh.org/trimesh.html), which you could save to glb/obj (or other
-format) file.
-
-For **Hunyuan3D-Paint**, do the following:
-
-```python
-from hy3dgen.texgen import Hunyuan3DPaintPipeline
-from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
-
-# let's generate a mesh first
-pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
-mesh = pipeline(image='assets/demo.png')[0]
-
-pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2')
-mesh = pipeline(mesh, image='assets/demo.png')
-```
-
-Please visit [minimal_demo.py](minimal_demo.py) for more advanced usage, such as **text to 3D** and **texture generation
-for handcrafted mesh**.
-
-### Gradio App
-
-You could also host a [Gradio](https://www.gradio.app/) App in your own computer via:
-
-```bash
-pip3 install gradio==3.39.0
-python3 gradio_app.py
-```
-
-Don't forget to visit [Hunyuan3D](https://3d.hunyuan.tencent.com) for quick use, if you don't want to host yourself.
-
-## 📑 Open-Source Plan
-
-- [x] Inference Code
-- [x] Model Checkpoints
-- [x] Technical Report
-- [ ] ComfyUI
-- [ ] TensorRT Version
-
-## 🔗 BibTeX
-
-If you found this repository helpful, please cite our report:
-
-```bibtex
-@misc{hunyuan3d22025tencent,
-    title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation},
-    author={Tencent Hunyuan3D Team},
-    year={2025},
-    eprint={2501.12202},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV}
-}
-
-@misc{yang2024tencent,
-    title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation},
-    author={Tencent Hunyuan3D Team},
-    year={2024},
-    eprint={2411.02293},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV}
-}
-```
-
-## Community Resources
-
-Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0:
-
-- [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper)
-- [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows)
-- [📦 A bundle for running on Windows | 整合包](https://github.com/YanWenKun/Comfy3D-WinPortable/releases/tag/r8-hunyuan3d2)
-
-## Acknowledgements
-
-We would like to thank the contributors to
-the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers)
-and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
-
-## Star History
-
-<a href="https://star-history.com/#Tencent/Hunyuan3D-2&Date">
- <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Tencent/Hunyuan3D-2&type=Date&theme=dark" />
-   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Tencent/Hunyuan3D-2&type=Date" />
-   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Tencent/Hunyuan3D-2&type=Date" />
- </picture>
-</a>
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/assets/demo.png b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/assets/demo.png
deleted file mode 100644
index 00fda1d..0000000
Binary files a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/assets/demo.png and /dev/null differ
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-fast/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-fast/config.yaml
new file mode 100644
index 0000000..7f52d46
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-fast/config.yaml
@@ -0,0 +1,69 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 16
+    depth_single_blocks: 32
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 0.9990943042622529
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoder # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-turbo/config.yaml
new file mode 100644
index 0000000..8db19f9
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-dit-v2-0-turbo/config.yaml
@@ -0,0 +1,70 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 16
+    depth_single_blocks: 32
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 0.9990943042622529
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoder # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.ConsistencyFlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+    pcm_timesteps: 100
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/.gitattributes b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/.gitattributes
new file mode 100644
index 0000000..a6344aa
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/README.md b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/README.md
new file mode 100644
index 0000000..ced35bb
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/README.md
@@ -0,0 +1,53 @@
+---
+license: openrail++
+tags:
+- stable-diffusion
+- text-to-image
+---
+
+# SD v2.1-base with Zero Terminal SNR (LAION Aesthetic 6+)
+
+This model is used in [Diffusion Model with Perceptual Loss](https://arxiv.org/abs/2401.00110) paper as the MSE baseline.
+
+This model is trained using zero terminal SNR schedule following [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/abs/2305.08891) paper on LAION aesthetic 6+ data.
+
+This model is finetuned from [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base).
+
+This model is meant for research demonstration, not for production use.
+
+## Usage
+
+```python
+from diffusers import StableDiffusionPipeline
+prompt = "A young girl smiling"
+pipe = StableDiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6").to("cuda")
+pipe(prompt, guidance_scale=7.5, guidance_rescale=0.7).images[0].save("out.jpg")
+```
+
+## Related Models
+
+* [bytedance/sd2.1-base-zsnr-laionaes5](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes5)
+* [bytedance/sd2.1-base-zsnr-laionaes6](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes6)
+* [bytedance/sd2.1-base-zsnr-laionaes6-perceptual](https://huggingface.co/ByteDance/sd2.1-base-zsnr-laionaes6-perceptual)
+
+
+## Cite as
+```
+@misc{lin2024diffusion,
+      title={Diffusion Model with Perceptual Loss}, 
+      author={Shanchuan Lin and Xiao Yang},
+      year={2024},
+      eprint={2401.00110},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{lin2023common,
+      title={Common Diffusion Noise Schedules and Sample Steps are Flawed}, 
+      author={Shanchuan Lin and Bingchen Liu and Jiashi Li and Xiao Yang},
+      year={2023},
+      eprint={2305.08891},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/feature_extractor/preprocessor_config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/feature_extractor/preprocessor_config.json
new file mode 100644
index 0000000..5294955
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/feature_extractor/preprocessor_config.json
@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/config.json
new file mode 100644
index 0000000..5e55ea1
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/config.json
@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "D:\\.cache\\huggingface\\hub\\models--sudo-ai--zero123plus-v1.1\\snapshots\\36df7de980afd15f80b2e1a4e9a920d7020e2654\\vision_encoder",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 32,
+  "patch_size": 14,
+  "projection_dim": 1024,
+  "torch_dtype": "float16",
+  "transformers_version": "4.36.0"
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json
new file mode 100644
index 0000000..fb1692f
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/image_encoder/preprocessor_config.json
@@ -0,0 +1,27 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/model_index.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/model_index.json
new file mode 100644
index 0000000..01d2c53
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/model_index.json
@@ -0,0 +1,37 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.23.1",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "requires_safety_checker": false,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "unet": [
+    "modules",
+    "UNet2p5DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/scheduler/scheduler_config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/scheduler/scheduler_config.json
new file mode 100644
index 0000000..e2cca4b
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/scheduler/scheduler_config.json
@@ -0,0 +1,15 @@
+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.23.1",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "set_alpha_to_one": true,
+  "steps_offset": 1,
+  "trained_betas": null,
+  "timestep_spacing": "trailing",
+  "rescale_betas_zero_snr": true
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/text_encoder/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/text_encoder/config.json
new file mode 100644
index 0000000..f9ac7c0
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/text_encoder/config.json
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "stabilityai/stable-diffusion-2",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.0.dev0",
+  "vocab_size": 49408
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/merges.txt b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/merges.txt
@@ -0,0 +1,48895 @@
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new file mode 100644
index 0000000..ae0c5be
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/special_tokens_map.json
@@ -0,0 +1,24 @@
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new file mode 100644
index 0000000..65d055d
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/tokenizer/tokenizer_config.json
@@ -0,0 +1,34 @@
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+    "content": "<|startoftext|>",
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+    "normalized": true,
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+  "errors": "replace",
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+  "name_or_path": "stabilityai/stable-diffusion-2",
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+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
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+  }
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new file mode 100644
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+  "ĮãĤĬãģŁãģĦ</w>": 47021,
+  "Įë": 17003,
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+  "į</w>": 491,
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+  "łï¸ı": 27899,
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+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/config.json
new file mode 100644
index 0000000..0fa07f9
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/config.json
@@ -0,0 +1,45 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.10.0.dev0",
+  "act_fn": "silu",
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "cross_attention_dim": 1024,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dual_cross_attention": false,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "sample_size": 64,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "use_linear_projection": true
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/modules.py b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/modules.py
new file mode 100644
index 0000000..36b4594
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/unet/modules.py
@@ -0,0 +1,610 @@
+# 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
+# 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 copy
+import json
+import os
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models import UNet2DConditionModel
+from diffusers.models.attention_processor import Attention
+from diffusers.models.transformers.transformer_2d import BasicTransformerBlock
+from einops import rearrange
+
+
+def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
+    # "feed_forward_chunk_size" can be used to save memory
+    if hidden_states.shape[chunk_dim] % chunk_size != 0:
+        raise ValueError(
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}"
+            f"has to be divisible by chunk size: {chunk_size}."
+            f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+        )
+
+    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
+    ff_output = torch.cat(
+        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+        dim=chunk_dim,
+    )
+    return ff_output
+
+
+class Basic2p5DTransformerBlock(torch.nn.Module):
+    def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True, is_turbo=False) -> None:
+        super().__init__()
+        self.transformer = transformer
+        self.layer_name = layer_name
+        self.use_ma = use_ma
+        self.use_ra = use_ra
+        self.is_turbo = is_turbo
+
+        # multiview attn
+        if self.use_ma:
+            self.attn_multiview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+        # ref attn
+        if self.use_ra:
+            self.attn_refview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+        if self.is_turbo:
+            self._initialize_attn_weights()
+
+    def _initialize_attn_weights(self):
+
+        if self.use_ma:
+            self.attn_multiview.load_state_dict(self.attn1.state_dict()) 
+            with torch.no_grad():
+                for layer in self.attn_multiview.to_out:
+                    for param in layer.parameters():
+                        param.zero_()
+        if self.use_ra:
+            self.attn_refview.load_state_dict(self.attn1.state_dict()) 
+            with torch.no_grad():
+                for layer in self.attn_refview.to_out:
+                    for param in layer.parameters():
+                        param.zero_()
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.transformer, name)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1)
+        mode = cross_attention_kwargs.pop('mode', None)
+        if not self.is_turbo:
+            mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
+            ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
+        else:
+            position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None)
+            position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None)
+            mva_scale = 1.0
+            ref_scale = 1.0
+            
+        condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)
+
+        if self.norm_type == "ada_norm":
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.norm_type == "ada_norm_zero":
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif self.norm_type == "ada_norm_single":
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Prepare GLIGEN inputs
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+        if self.norm_type == "ada_norm_zero":
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.norm_type == "ada_norm_single":
+            attn_output = gate_msa * attn_output
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 1.2 Reference Attention
+        if 'w' in mode:
+            condition_embed_dict[self.layer_name] = rearrange(
+                norm_hidden_states, '(b n) l c -> b (n l) c',
+                n=num_in_batch
+            )  # B, (N L), C
+
+        if 'r' in mode and self.use_ra:
+            condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1,
+                                                                                        1)  # B N L C
+            condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c')
+
+            attn_output = self.attn_refview(
+                norm_hidden_states,
+                encoder_hidden_states=condition_embed,
+                attention_mask=None,
+                **cross_attention_kwargs
+            )
+            if not self.is_turbo:
+                ref_scale_timing = ref_scale
+                if isinstance(ref_scale, torch.Tensor):
+                    ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
+                    for _ in range(attn_output.ndim - 1):
+                        ref_scale_timing = ref_scale_timing.unsqueeze(-1)
+                        
+            hidden_states = ref_scale_timing * attn_output + hidden_states
+
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+
+        # 1.3 Multiview Attention
+        if num_in_batch > 1 and self.use_ma:
+            multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch)
+
+            if self.is_turbo:
+                position_mask = None
+                if position_attn_mask is not None:
+                    if multivew_hidden_states.shape[1] in position_attn_mask:
+                        position_mask = position_attn_mask[multivew_hidden_states.shape[1]]
+                position_indices = None
+                if position_voxel_indices is not None:
+                    if multivew_hidden_states.shape[1] in position_voxel_indices:
+                        position_indices = position_voxel_indices[multivew_hidden_states.shape[1]]
+                attn_output = self.attn_multiview(
+                    multivew_hidden_states,
+                    encoder_hidden_states=multivew_hidden_states,
+                    attention_mask=position_mask,
+                    position_indices=position_indices,
+                    **cross_attention_kwargs
+                )
+            else:
+                attn_output = self.attn_multiview(
+                    multivew_hidden_states,
+                    encoder_hidden_states=multivew_hidden_states,
+                    **cross_attention_kwargs
+                )
+
+            attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch)
+            
+            hidden_states = mva_scale * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+
+        # 1.2 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            if self.norm_type == "ada_norm":
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.norm_type == "ada_norm_single":
+                # For PixArt norm2 isn't applied here:
+                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+                norm_hidden_states = hidden_states
+            elif self.norm_type == "ada_norm_continuous":
+                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
+            else:
+                raise ValueError("Incorrect norm")
+
+            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        # i2vgen doesn't have this norm 🤷‍♂️
+        if self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif not self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.norm_type == "ada_norm_single":
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+@torch.no_grad()
+def compute_voxel_grid_mask(position, grid_resolution=8):
+
+    position = position.half()    
+    B,N,_,H,W = position.shape
+    assert H%grid_resolution==0 and W%grid_resolution==0
+
+    valid_mask = (position != 1).all(dim=2, keepdim=True)
+    valid_mask = valid_mask.expand_as(position)
+    position[valid_mask==False] = 0
+
+    
+    position = rearrange(
+        position,
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+    valid_mask = rearrange(
+        valid_mask, 
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+
+    grid_position = position.sum(dim=(-2, -1))
+    count_masked = valid_mask.sum(dim=(-2, -1))
+
+    grid_position = grid_position / count_masked.clamp(min=1)
+    grid_position[count_masked<5] = 0
+
+    grid_position = grid_position.permute(0,1,4,2,3)
+    grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c')
+
+    grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4)  # 形状变为 B, N, 1, L, 1, 3
+    grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3)  # 形状变为 B, 1, N, 1, L, 3
+
+    # 计算欧氏距离
+    distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1)  # 形状为 B, N, N, L, L
+
+    weights = distances
+    grid_distance = 1.73/grid_resolution
+    
+    #weights = weights*-32
+    #weights = weights.clamp(min=-10000.0)
+    
+    weights = weights< grid_distance
+
+    return weights
+    
+def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]):
+    position_attn_mask = {}
+    with torch.no_grad():
+        for grid_resolution in grid_resolutions:
+            position_mask = compute_voxel_grid_mask(position_maps, grid_resolution)
+            position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)')
+            position_attn_mask[position_mask.shape[1]] = position_mask
+    return position_attn_mask
+
+@torch.no_grad()
+def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128):
+
+    position = position.half()    
+    B,N,_,H,W = position.shape
+    assert H%grid_resolution==0 and W%grid_resolution==0
+
+    valid_mask = (position != 1).all(dim=2, keepdim=True)
+    valid_mask = valid_mask.expand_as(position)
+    position[valid_mask==False] = 0
+    
+    position = rearrange(
+        position, 
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+    valid_mask = rearrange(
+        valid_mask, 
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+
+    grid_position = position.sum(dim=(-2, -1))
+    count_masked = valid_mask.sum(dim=(-2, -1))
+
+    grid_position = grid_position / count_masked.clamp(min=1)
+    grid_position[count_masked<5] = 0
+
+    grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W
+    voxel_indices = grid_position * (voxel_resolution - 1)
+    voxel_indices = torch.round(voxel_indices).long()
+    return voxel_indices
+    
+def compute_multi_resolution_discrete_voxel_indice(
+    position_maps, 
+    grid_resolutions=[64, 32, 16, 8], 
+    voxel_resolutions=[512, 256, 128, 64]
+):
+    voxel_indices = {}
+    with torch.no_grad():
+        for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions):
+            voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution)
+            voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c')
+            voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution}
+    return voxel_indices
+
+class UNet2p5DConditionModel(torch.nn.Module):
+    def __init__(self, unet: UNet2DConditionModel) -> None:
+        super().__init__()
+        self.unet = unet
+
+        self.use_ma = True
+        self.use_ra = True
+        self.use_camera_embedding = True
+        self.use_dual_stream = True
+        self.is_turbo = False
+
+        if self.use_dual_stream:
+            self.unet_dual = copy.deepcopy(unet)
+            self.init_attention(self.unet_dual)
+        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo)
+        self.init_condition()
+        self.init_camera_embedding()
+
+    @staticmethod
+    def from_pretrained(pretrained_model_name_or_path, **kwargs):
+        torch_dtype = kwargs.pop('torch_dtype', torch.float32)
+        config_path = os.path.join(pretrained_model_name_or_path, 'config.json')
+        unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin')
+        with open(config_path, 'r', encoding='utf-8') as file:
+            config = json.load(file)
+        unet = UNet2DConditionModel(**config)
+        unet = UNet2p5DConditionModel(unet)
+        unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True)
+        unet.load_state_dict(unet_ckpt, strict=True)
+        unet = unet.to(torch_dtype)
+        return unet
+
+    def init_condition(self):
+        self.unet.conv_in = torch.nn.Conv2d(
+            12,
+            self.unet.conv_in.out_channels,
+            kernel_size=self.unet.conv_in.kernel_size,
+            stride=self.unet.conv_in.stride,
+            padding=self.unet.conv_in.padding,
+            dilation=self.unet.conv_in.dilation,
+            groups=self.unet.conv_in.groups,
+            bias=self.unet.conv_in.bias is not None)
+
+        self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1, 77, 1024))
+        self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1, 77, 1024))
+
+    def init_camera_embedding(self):
+
+        if self.use_camera_embedding:
+            time_embed_dim = 1280
+            self.max_num_ref_image = 5
+            self.max_num_gen_image = 12 * 3 + 4 * 2
+            self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim)
+
+    def init_attention(self, unet, use_ma=False, use_ra=False, is_turbo=False):
+
+        for down_block_i, down_block in enumerate(unet.down_blocks):
+            if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention:
+                for attn_i, attn in enumerate(down_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(
+                                transformer,
+                                f'down_{down_block_i}_{attn_i}_{transformer_i}',
+                                use_ma, use_ra, is_turbo
+                            )
+
+        if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention:
+            for attn_i, attn in enumerate(unet.mid_block.attentions):
+                for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                    if isinstance(transformer, BasicTransformerBlock):
+                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(
+                            transformer,
+                            f'mid_{attn_i}_{transformer_i}',
+                            use_ma, use_ra, is_turbo
+                        )
+
+        for up_block_i, up_block in enumerate(unet.up_blocks):
+            if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention:
+                for attn_i, attn in enumerate(up_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(
+                                transformer,
+                                f'up_{up_block_i}_{attn_i}_{transformer_i}',
+                                use_ma, use_ra, is_turbo
+                            )
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+
+    def forward(
+        self, sample, timestep, encoder_hidden_states,
+        *args, down_intrablock_additional_residuals=None,
+        down_block_res_samples=None, mid_block_res_sample=None,
+        **cached_condition,
+    ):
+        B, N_gen, _, H, W = sample.shape
+        assert H == W
+
+        if self.use_camera_embedding:
+            camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image
+            camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)')
+        else:
+            camera_info_gen = None
+
+        sample = [sample]
+        if 'normal_imgs' in cached_condition:
+            sample.append(cached_condition["normal_imgs"])
+        if 'position_imgs' in cached_condition:
+            sample.append(cached_condition["position_imgs"])
+        sample = torch.cat(sample, dim=2)
+
+        sample = rearrange(sample, 'b n c h w -> (b n) c h w')
+
+        encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1)
+        encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c')
+
+        if self.use_ra:
+            if 'condition_embed_dict' in cached_condition:
+                condition_embed_dict = cached_condition['condition_embed_dict']
+            else:
+                condition_embed_dict = {}
+                ref_latents = cached_condition['ref_latents']
+                N_ref = ref_latents.shape[1]
+                if self.use_camera_embedding:
+                    camera_info_ref = cached_condition['camera_info_ref']
+                    camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)')
+                else:
+                    camera_info_ref = None
+
+                ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w')
+
+                encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1)
+                encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c')
+
+                noisy_ref_latents = ref_latents
+                timestep_ref = 0
+
+                if self.use_dual_stream:
+                    unet_ref = self.unet_dual
+                else:
+                    unet_ref = self.unet
+                unet_ref(
+                    noisy_ref_latents, timestep_ref,
+                    encoder_hidden_states=encoder_hidden_states_ref,
+                    class_labels=camera_info_ref,
+                    # **kwargs
+                    return_dict=False,
+                    cross_attention_kwargs={
+                        'mode': 'w', 'num_in_batch': N_ref,
+                        'condition_embed_dict': condition_embed_dict},
+                )
+                cached_condition['condition_embed_dict'] = condition_embed_dict
+        else:
+            condition_embed_dict = None
+
+        mva_scale = cached_condition.get('mva_scale', 1.0)
+        ref_scale = cached_condition.get('ref_scale', 1.0)
+
+        if self.is_turbo:
+            cross_attention_kwargs_ = {
+                'mode': 'r', 'num_in_batch': N_gen,
+                'condition_embed_dict': condition_embed_dict,
+                'position_attn_mask':position_attn_mask, 
+                'position_voxel_indices':position_voxel_indices,
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            }
+        else:
+            cross_attention_kwargs_ = {
+                'mode': 'r', 'num_in_batch': N_gen,
+                'condition_embed_dict': condition_embed_dict,
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            }
+        return self.unet(
+            sample, timestep,
+            encoder_hidden_states_gen, *args,
+            class_labels=camera_info_gen,
+            down_intrablock_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals
+            ] if down_intrablock_additional_residuals is not None else None,
+            down_block_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples
+            ] if down_block_res_samples is not None else None,
+            mid_block_additional_residual=(
+                mid_block_res_sample.to(dtype=self.unet.dtype)
+                if mid_block_res_sample is not None else None
+            ),
+            return_dict=False,
+            cross_attention_kwargs=cross_attention_kwargs_,
+        )
+
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/vae/config.json b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/vae/config.json
new file mode 100644
index 0000000..9421ac5
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-paint-v2-0-turbo/vae/config.json
@@ -0,0 +1,29 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.10.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 768,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0-turbo/config.yaml
new file mode 100644
index 0000000..4e84fc2
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0-turbo/config.yaml
@@ -0,0 +1,15 @@
+target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+params:
+  num_latents: 3072
+  embed_dim: 64
+  num_freqs: 8
+  include_pi: false
+  heads: 16
+  width: 1024
+  num_decoder_layers: 16
+  qkv_bias: false
+  qk_norm: true
+  scale_factor: 0.9990943042622529
+  geo_decoder_mlp_expand_ratio: 1
+  geo_decoder_downsample_ratio: 2
+  geo_decoder_ln_post: false
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0/config.yaml
new file mode 100644
index 0000000..f0ff063
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2/hunyuan3d-vae-v2-0/config.yaml
@@ -0,0 +1,15 @@
+target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+params:
+  num_latents: 3072
+  embed_dim: 64
+  num_freqs: 8
+  include_pi: false
+  heads: 16
+  width: 1024
+  num_decoder_layers: 16
+  qkv_bias: false
+  qk_norm: true
+  scale_factor: 0.9990943042622529
+  geo_decoder_mlp_expand_ratio: 4
+  geo_decoder_downsample_ratio: 1
+  geo_decoder_ln_post: true
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-fast/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-fast/config.yaml
new file mode 100644
index 0000000..65890be
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-fast/config.yaml
@@ -0,0 +1,69 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 8
+    depth_single_blocks: 16
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 512
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 1.0188137142395404
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoder # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-turbo/config.yaml
new file mode 100644
index 0000000..0ad6426
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini-turbo/config.yaml
@@ -0,0 +1,70 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 8
+    depth_single_blocks: 16
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 1.0188137142395404
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoder # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 1022
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.ConsistencyFlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+    pcm_timesteps: 100
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2
+  params:
+    size: 1022
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini/config.yaml
new file mode 100644
index 0000000..4c255d8
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mini/hunyuan3d-dit-v2-mini/config.yaml
@@ -0,0 +1,69 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 8
+    depth_single_blocks: 16
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: false
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 512
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 1.0188137142395404
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoder # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml
new file mode 100644
index 0000000..0cb7655
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-fast/config.yaml
@@ -0,0 +1,69 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 16
+    depth_single_blocks: 32
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 0.9990943042622529
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoderMV # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.MVImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
\ No newline at end of file
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-turbo/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-turbo/config.yaml
new file mode 100644
index 0000000..2df2d51
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv-turbo/config.yaml
@@ -0,0 +1,70 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 16
+    depth_single_blocks: 32
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+    guidance_embed: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 0.9990943042622529
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoderMV # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.ConsistencyFlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+    pcm_timesteps: 100
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.MVImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
diff --git a/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml
new file mode 100644
index 0000000..29fac42
--- /dev/null
+++ b/Checkpoints/Diffusers/tencent/Hunyuan3D-2mv/hunyuan3d-dit-v2-mv/config.yaml
@@ -0,0 +1,68 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 16
+    depth_single_blocks: 32
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: true
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 0.9990943042622529
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoderMV # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.MVImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
\ No newline at end of file
diff --git a/Checkpoints/facebookresearch/dinov2/Put dinov2 here.txt b/Checkpoints/facebookresearch/dinov2/Put dinov2 here.txt
new file mode 100644
index 0000000..e69de29
diff --git a/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_ig2mv_partialimg_sdxl.yaml b/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_ig2mv_partialimg_sdxl.yaml
new file mode 100644
index 0000000..85fea0e
--- /dev/null
+++ b/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_ig2mv_partialimg_sdxl.yaml
@@ -0,0 +1,123 @@
+name: ig2mv
+tag: "r768-ortho-nv6-ig2mv-partialimg-dcrowcol-sdxl"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: data/texture_ortho6view_easylight
+  scene_list: data/objaverse_list_6w.json
+  background_color: gray
+  image_names: ["0000", "0001", "0002", "0003", "0008", "0009"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["position", "normal"]
+  position_offset: 0.5
+  position_scale: 1.0
+
+  reference_root_dir: ["data/texture_rand_easylight_objaverse"]
+  reference_scene_list: ["data/objaverse_list_6w.json"]
+  reference_image_modality: color
+  reference_image_names: ["0000", "0001", "0002", "0003", "0004"]
+  reference_mask_aug: true
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 768
+  width: 768
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_image_sdxl.MVAdapterImageSDXLSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-xl-base-1.0"
+  pretrained_vae_name_or_path: "madebyollin/sdxl-vae-fp16-fix"
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowColSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv", "to_out_ref"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv", "_ref"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_ig2mv_sdxl.yaml b/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_ig2mv_sdxl.yaml
new file mode 100644
index 0000000..83d46e7
--- /dev/null
+++ b/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_ig2mv_sdxl.yaml
@@ -0,0 +1,122 @@
+name: ig2mv
+tag: "r768-ortho-nv6-ig2mv-dcrowcol-sdxl"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: data/texture_ortho6view_easylight
+  scene_list: data/objaverse_list_6w.json
+  background_color: gray
+  image_names: ["0000", "0001", "0002", "0003", "0008", "0009"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["position", "normal"]
+  position_offset: 0.5
+  position_scale: 1.0
+
+  reference_root_dir: ["data/texture_rand_easylight_objaverse"]
+  reference_scene_list: ["data/objaverse_list_6w.json"]
+  reference_image_modality: color
+  reference_image_names: ["0000", "0001", "0002", "0003", "0004"]
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 768
+  width: 768
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_image_sdxl.MVAdapterImageSDXLSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-xl-base-1.0"
+  pretrained_vae_name_or_path: "madebyollin/sdxl-vae-fp16-fix"
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowColSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv", "to_out_ref"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv", "_ref"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_tg2mv_sdxl.yaml b/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_tg2mv_sdxl.yaml
new file mode 100644
index 0000000..2861767
--- /dev/null
+++ b/Configs/MV_Adapter_configs/geometry-guidance/mvadapter_tg2mv_sdxl.yaml
@@ -0,0 +1,117 @@
+name: tg2mv
+tag: "r768-ortho-nv6-tg2mv-dcrowcol-sdxl"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: data/texture_ortho10view_easylight_objaverse
+  scene_list: data/objaverse_list_6w.json
+  background_color: gray
+  image_names: ["0000", "0001", "0002", "0003", "0008", "0009"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["position", "normal"]
+  position_offset: 0.5
+  position_scale: 1.0
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 768
+  width: 768
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_text_sdxl.MVAdapterTextSDXLSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-xl-base-1.0"
+  pretrained_vae_name_or_path: "madebyollin/sdxl-vae-fp16-fix"
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowColSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sd21.yaml b/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sd21.yaml
new file mode 100644
index 0000000..6ef9eac
--- /dev/null
+++ b/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sd21.yaml
@@ -0,0 +1,122 @@
+name: i2mv
+tag: "r512-ortho-nv6-ele0-sd21"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: "data/texture_ortho10view_easylight_objaverse"
+  scene_list: "data/objaverse_list_6w.json"
+  background_color: gray
+  image_names: ["0000", "0004", "0001", "0002", "0003", "0005"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["plucker"]
+  plucker_offset: 1.0
+  plucker_scale: 2.0
+
+  reference_root_dir: ["data/texture_rand_easylight_objaverse"]
+  reference_scene_list: ["data/objaverse_list_6w.json"]
+  reference_image_modality: color
+  reference_image_names: ["0000", "0001", "0002", "0003", "0004"]
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 512
+  width: 512
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_image_sd.MVAdapterImageSDSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
+  pretrained_vae_name_or_path: null
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv", "to_out_ref"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv", "_ref"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 5
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sdxl.yaml b/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sdxl.yaml
new file mode 100644
index 0000000..db4e20e
--- /dev/null
+++ b/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sdxl.yaml
@@ -0,0 +1,122 @@
+name: i2mv
+tag: "r768-ortho-nv6-ele0-sdxl"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: "data/texture_ortho10view_easylight_objaverse"
+  scene_list: "data/objaverse_list_6w.json"
+  background_color: gray
+  image_names: ["0000", "0004", "0001", "0002", "0003", "0005"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["plucker"]
+  plucker_offset: 1.0
+  plucker_scale: 2.0
+
+  reference_root_dir: ["data/texture_rand_easylight_objaverse"]
+  reference_scene_list: ["data/objaverse_list_6w.json"]
+  reference_image_modality: color
+  reference_image_names: ["0000", "0001", "0002", "0003", "0004"]
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 768
+  width: 768
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_image_sdxl.MVAdapterImageSDXLSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-xl-base-1.0"
+  pretrained_vae_name_or_path: "madebyollin/sdxl-vae-fp16-fix"
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv", "to_out_ref"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv", "_ref"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sdxl_aug_quantity.yaml b/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sdxl_aug_quantity.yaml
new file mode 100644
index 0000000..de1ac61
--- /dev/null
+++ b/Configs/MV_Adapter_configs/view-guidance/mvadapter_i2mv_sdxl_aug_quantity.yaml
@@ -0,0 +1,123 @@
+name: i2mv
+tag: "r768-ortho-nv6-ele0-sdxl-aug-quantity"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: "data/texture_ortho10view_easylight_objaverse"
+  scene_list: "data/objaverse_list_6w.json"
+  background_color: gray
+  image_names: ["0000", "0004", "0001", "0002", "0003", "0005"]
+  image_modality: color
+  num_views: 6
+  random_view_list: [[0, 1, 2, 3, 4, 5], [0, 2, 3, 4], [0, 2, 3], [0, 3]]
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["plucker"]
+  plucker_offset: 1.0
+  plucker_scale: 2.0
+
+  reference_root_dir: ["data/texture_rand_easylight_objaverse"]
+  reference_scene_list: ["data/objaverse_list_6w.json"]
+  reference_image_modality: color
+  reference_image_names: ["0000", "0001", "0002", "0003", "0004"]
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 768
+  width: 768
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_image_sdxl.MVAdapterImageSDXLSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-xl-base-1.0"
+  pretrained_vae_name_or_path: "madebyollin/sdxl-vae-fp16-fix"
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv", "to_out_ref"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv", "_ref"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/view-guidance/mvadapter_t2mv_sd21.yaml b/Configs/MV_Adapter_configs/view-guidance/mvadapter_t2mv_sd21.yaml
new file mode 100644
index 0000000..4e64c2e
--- /dev/null
+++ b/Configs/MV_Adapter_configs/view-guidance/mvadapter_t2mv_sd21.yaml
@@ -0,0 +1,117 @@
+name: t2mv
+tag: "r512-ortho-nv6-ele0-sd"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: "data/texture_ortho10view_easylight_objaverse"
+  scene_list: "data/objaverse_list_6w.json"
+  background_color: gray
+  image_names: ["0000", "0004", "0001", "0002", "0003", "0005"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["plucker"]
+  plucker_offset: 1.0
+  plucker_scale: 2.0
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 512
+  width: 512
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_text_sd.MVAdapterTextSDSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-2-1-base"
+  pretrained_vae_name_or_path: null
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Configs/MV_Adapter_configs/view-guidance/mvadapter_t2mv_sdxl.yaml b/Configs/MV_Adapter_configs/view-guidance/mvadapter_t2mv_sdxl.yaml
new file mode 100644
index 0000000..bb2b323
--- /dev/null
+++ b/Configs/MV_Adapter_configs/view-guidance/mvadapter_t2mv_sdxl.yaml
@@ -0,0 +1,117 @@
+name: t2mv
+tag: "r768-ortho-nv6-ele0-sdxl"
+exp_root_dir: "outputs"
+seed: 42
+
+data_cls: mvadapter.data.multiview.MultiviewDataModule
+data:
+  root_dir: "data/texture_ortho10view_easylight_objaverse"
+  scene_list: "data/objaverse_list_6w.json"
+  background_color: gray
+  image_names: ["0000", "0004", "0001", "0002", "0003", "0005"]
+  image_modality: color
+  num_views: 6
+
+  prompt_db_path: data/objaverse_short_captions.json
+  return_prompt: true
+
+  projection_type: ORTHO
+
+  source_image_modality: ["plucker"]
+  plucker_offset: 1.0
+  plucker_scale: 2.0
+
+  train_indices: [0, -8]
+  val_indices: [-8, null]
+  test_indices: [-8, null]
+
+  height: 768
+  width: 768
+
+  batch_size: 1
+  num_workers: 16
+
+system_cls: mvadapter.systems.mvadapter_text_sdxl.MVAdapterTextSDXLSystem
+system:
+  check_train_every_n_steps: 1000
+  cleanup_after_validation_step: true
+  cleanup_after_test_step: true
+
+  # Model / Adapter
+  pretrained_model_name_or_path: "stabilityai/stable-diffusion-xl-base-1.0"
+  pretrained_vae_name_or_path: "madebyollin/sdxl-vae-fp16-fix"
+  pretrained_adapter_name_or_path: null
+  init_adapter_kwargs:
+    # Multi-view adapter
+    self_attn_processor: "mvadapter.models.attention_processor.DecoupledMVRowSelfAttnProcessor2_0"
+    # Condition encoder
+    cond_in_channels: 6
+    # For training
+    copy_attn_weights: true
+    zero_init_module_keys: ["to_out_mv"]
+
+  # Training
+  train_cond_encoder: true
+  trainable_modules: ["_mv"]
+  prompt_drop_prob: 0.1
+  image_drop_prob: 0.1
+  cond_drop_prob: 0.1
+
+  # Noise sampler
+  shift_noise: true
+  shift_noise_mode: interpolated
+  shift_noise_scale: 8
+
+  # Evaluation
+  eval_seed: 42
+  eval_num_inference_steps: 30
+  eval_guidance_scale: 3.0
+  eval_height: ${data.height}
+  eval_width: ${data.width}
+
+  # optimizer definition
+  # you can set different learning rates separately for each group of parameters, but note that if you do this you should specify EVERY trainable parameters
+  optimizer:
+    name: AdamW
+    args:
+      lr: 5e-5
+      betas: [0.9, 0.999]
+      weight_decay: 0.01
+    params:
+      cond_encoder:
+        lr: 5e-5
+      unet:
+        lr: 5e-5
+
+  scheduler:
+    name: SequentialLR
+    interval: step
+    schedulers:
+      - name: LinearLR
+        interval: step
+        args:
+          start_factor: 1e-6
+          end_factor: 1.0
+          total_iters: 2000
+      - name: ConstantLR
+        interval: step
+        args:
+          factor: 1.0
+          total_iters: 9999999
+    milestones: [2000]
+
+trainer:
+  max_epochs: 10
+  log_every_n_steps: 10
+  num_sanity_val_steps: 1
+  val_check_interval: 2000
+  enable_progress_bar: true
+  precision: bf16-mixed
+  gradient_clip_val: 1.0
+  strategy: ddp
+  accumulate_grad_batches: 1
+
+checkpoint:
+  save_last: true # whether to save at each validation time
+  save_top_k: -1
+  every_n_epochs: 9999 # do not save at all for debug purpose
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/.gitignore b/Gen_3D_Modules/Hunyuan3D_V2/.gitignore
new file mode 100644
index 0000000..35000cb
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/.gitignore
@@ -0,0 +1,169 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+!hy3dgen/texgen/custom_rasterizer/lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+.DS_Store
+# Cython debug symbols
+cython_debug/
+gradio_cache/
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/LICENSE b/Gen_3D_Modules/Hunyuan3D_V2/LICENSE
new file mode 100644
index 0000000..46b31e7
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/LICENSE
@@ -0,0 +1,80 @@
+TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT
+Tencent Hunyuan 3D 2.0 Release Date: January 21, 2025
+THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KOREA AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
+By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan 3D 2.0 Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+1.	DEFINITIONS.
+a.	“Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
+b.	“Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of Tencent Hunyuan 3D 2.0 Works or any portion or element thereof set forth herein.
+c.	“Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan 3D 2.0 made publicly available by Tencent.
+d.	“Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
+e.	“Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent Hunyuan 3D 2.0 Works for any purpose and in any field of use.
+f.	“Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan 3D 2.0 and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
+g.	“Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan 3D 2.0 or any Model Derivative of Tencent Hunyuan 3D 2.0; (ii) works based on Tencent Hunyuan 3D 2.0 or any Model Derivative of Tencent Hunyuan 3D 2.0; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan 3D 2.0 or any Model Derivative of Tencent Hunyuan 3D 2.0, to that model in order to cause that model to perform similarly to Tencent Hunyuan 3D 2.0 or a Model Derivative of Tencent Hunyuan 3D 2.0, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan 3D 2.0 or a Model Derivative of Tencent Hunyuan 3D 2.0 for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
+h.	“Output” shall mean the information and/or content output of Tencent Hunyuan 3D 2.0 or a Model Derivative that results from operating or otherwise using Tencent Hunyuan 3D 2.0 or a Model Derivative, including via a Hosted Service.
+i.	“Tencent,” “We” or “Us” shall mean THL A29 Limited.
+j.	“Tencent Hunyuan 3D 2.0” shall mean the 3D generation 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 Us at https://github.com/Tencent/Hunyuan3D-2.
+k.	“Tencent Hunyuan 3D 2.0 Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof.
+l.	“Territory” shall mean the worldwide territory, excluding the territory of the European Union, United Kingdom and South Korea. 
+m.	“Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You.
+n.	“including” shall mean including but not limited to.
+2.	GRANT OF RIGHTS.
+We grant You, for the Territory only, a non-exclusive, non-transferable and royalty-free limited license under Tencent’s intellectual property or other rights owned by Us embodied in or utilized by the Materials to use, reproduce, distribute, create derivative works of (including Model Derivatives), and make modifications to the Materials, only in accordance with the terms of this Agreement and the Acceptable Use Policy, and You must not violate (or encourage or permit anyone else to violate) any term of this Agreement or the Acceptable Use Policy.
+3.	DISTRIBUTION.
+You may, subject to Your compliance with this Agreement, distribute or make available to Third Parties the Tencent Hunyuan 3D 2.0 Works, exclusively in the Territory, provided that You meet all of the following conditions:
+a.	You must provide all such Third Party recipients of the Tencent Hunyuan 3D 2.0 Works or products or services using them a copy of this Agreement;
+b.	You must cause any modified files to carry prominent notices stating that You changed the files;
+c.	You are encouraged to: (i) publish at least one technology introduction blogpost or one public statement expressing Your experience of using the Tencent Hunyuan 3D 2.0 Works; and (ii) mark the products or services developed by using the Tencent Hunyuan 3D 2.0 Works to indicate that the product/service is “Powered by Tencent Hunyuan”; and
+d.	All distributions to Third Parties (other than through a Hosted Service) must be accompanied by a “Notice” text file that contains the following notice: “Tencent Hunyuan 3D 2.0 is licensed under the Tencent Hunyuan 3D 2.0 Community License Agreement, Copyright © 2025 Tencent. All Rights Reserved. The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.”
+You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement (including as regards the Territory). If You receive Tencent Hunyuan 3D 2.0 Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You.
+4.	ADDITIONAL COMMERCIAL TERMS.
+If, on the Tencent Hunyuan 3D 2.0 version release date, the monthly active users of all products or services made available by or for Licensee is greater than 1 million monthly active users in the preceding calendar month, You must request a license from Tencent, which Tencent may grant to You in its sole discretion, and You are not authorized to exercise any of the rights under this Agreement unless or until Tencent otherwise expressly grants You such rights. 
+Subject to Tencent's written approval, you may request a license for the use of Tencent Hunyuan 3D 2.0 by submitting the following information to hunyuan3d@tencent.com:
+a.	Your company’s name and associated business sector that plans to use Tencent Hunyuan 3D 2.0.
+b.	Your intended use case and the purpose of using Tencent Hunyuan 3D 2.0.
+5.	RULES OF USE.
+a.	Your use of the Tencent Hunyuan 3D 2.0 Works must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Tencent Hunyuan 3D 2.0 Works, which is hereby incorporated by reference into this Agreement. You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan 3D 2.0 Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan 3D 2.0 Works are subject to the use restrictions in these Sections 5(a) and 5(b).
+b.	You must not use the Tencent Hunyuan 3D 2.0 Works or any Output or results of the Tencent Hunyuan 3D 2.0 Works to improve any other AI model (other than Tencent Hunyuan 3D 2.0 or Model Derivatives thereof).
+c.	You must not use, reproduce, modify, distribute, or display the Tencent Hunyuan 3D 2.0 Works, Output or results of the Tencent Hunyuan 3D 2.0 Works outside the Territory. Any such use outside the Territory is unlicensed and unauthorized under this Agreement.
+6.	INTELLECTUAL PROPERTY.
+a.	Subject to Tencent’s ownership of Tencent Hunyuan 3D 2.0 Works made by or for Tencent and intellectual property rights therein, conditioned upon Your compliance with the terms and conditions of this Agreement, as between You and Tencent, You will be the owner of any derivative works and modifications of the Materials and any Model Derivatives that are made by or for You.
+b.	No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan 3D 2.0 Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan 3D 2.0 Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) in the Territory solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent.
+c.	If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan 3D 2.0 Works.
+d.	Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses.
+7.	DISCLAIMERS OF WARRANTY AND LIMITATIONS OF LIABILITY.
+a.	We are not obligated to support, update, provide training for, or develop any further version of the Tencent Hunyuan 3D 2.0 Works or to grant any license thereto.
+b.	UNLESS AND ONLY TO THE EXTENT REQUIRED BY APPLICABLE LAW, THE TENCENT HUNYUAN 3D 2.0 WORKS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED “AS IS” WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES OF ANY KIND INCLUDING ANY WARRANTIES OF TITLE, MERCHANTABILITY, NONINFRINGEMENT, COURSE OF DEALING, USAGE OF TRADE, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR OR A THIRD PARTY’S USE OR DISTRIBUTION OF ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT.
+c.	TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL TENCENT OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, FOR ANY DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS, EVEN IF TENCENT OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+8.	SURVIVAL AND TERMINATION.
+a.	The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
+b.	We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan 3D 2.0 Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
+9.	GOVERNING LAW AND JURISDICTION.
+a.	This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+b.	Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
+ 
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: November 5, 2024
+
+Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan 3D 2.0. You agree not to use Tencent Hunyuan 3D 2.0 or Model Derivatives:
+1.	Outside the Territory;
+2.	In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
+3.	To harm Yourself or others;
+4.	To repurpose or distribute output from Tencent Hunyuan 3D 2.0 or any Model Derivatives to harm Yourself or others; 
+5.	To override or circumvent the safety guardrails and safeguards We have put in place;
+6.	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+7.	To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
+8.	To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
+9.	To intentionally defame, disparage or otherwise harass others;
+10.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+11.	To generate or disseminate personal identifiable information with the purpose of harming others;
+12.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+13.	To impersonate another individual without consent, authorization, or legal right;
+14.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+15.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+16.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+17.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+18.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+19.	For military purposes;
+20.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/NOTICE b/Gen_3D_Modules/Hunyuan3D_V2/NOTICE
new file mode 100644
index 0000000..e02234d
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/NOTICE
@@ -0,0 +1,214 @@
+Usage and Legal Notices:
+
+Tencent is pleased to support the open source community by making Hunyuan 3D 2.0 available.
+
+Copyright (C) 2025 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 2.0 is licensed under the TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT except for the third-party components listed below, which is licensed under different terms. Hunyuan 3D 2.0 does not impose any additional limitations beyond what is outlined in the respective 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 2.0 means inference-enabling code, parameters, and weights of this Model only, which are made publicly available by Tencent in accordance with TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT.
+
+
+Other dependencies and licenses:
+
+
+Open Source Model Licensed under the MIT and CreativeML Open RAIL++-M License:
+--------------------------------------------------------------------
+1. Stable Diffusion
+Copyright (c) 2022 Stability AI
+
+
+Terms of the MIT and CreativeML Open RAIL++-M License:
+--------------------------------------------------------------------
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+CreativeML Open RAIL++-M License
+dated November 24, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
+- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
+- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
+- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
+- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
+- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
+- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
+- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
+- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
+- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+
+Section II: INTELLECTUAL PROPERTY RIGHTS
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
+You must cause any modified files to carry prominent notices stating that You changed the files;
+You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
+6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
+10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+
+END OF TERMS AND CONDITIONS
+
+
+
+
+Attachment A
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
+- In any way that violates any applicable national, federal, state, local or international law or regulation;
+- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
+- To generate or disseminate personal identifiable information that can be used to harm an individual;
+- To defame, disparage or otherwise harass others;
+- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
+- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
+- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
+- To provide medical advice and medical results interpretation;
+- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
+
+
+
+Open Source Model Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT and Other Licenses of the Third-Party Components therein:
+--------------------------------------------------------------------
+1. HunyuanDiT
+Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved. 
+
+
+Terms of the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT:
+--------------------------------------------------------------------
+TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
+Tencent Hunyuan Release Date: 2024/5/14
+By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+1.	DEFINITIONS.
+a.	“Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
+b.	“Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of the Hunyuan Works or any portion or element thereof set forth herein.
+c.	“Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan made publicly available by Tencent.
+d.	“Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
+e.	“Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent Hunyuan Works for any purpose and in any field of use.
+f.	“Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
+g.	“Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; (ii) works based on Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan or any Model Derivative of Tencent Hunyuan, to that model in order to cause that model to perform similarly to Tencent Hunyuan or a Model Derivative of Tencent Hunyuan, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan or a Model Derivative of Tencent Hunyuan for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
+h.	“Output” shall mean the information and/or content output of Tencent Hunyuan or a Model Derivative that results from operating or otherwise using Tencent Hunyuan or a Model Derivative, including via a Hosted Service.
+i.	“Tencent,” “We” or “Us” shall mean THL A29 Limited.
+j.	“Tencent Hunyuan” shall mean the large language models, image/video/audio/3D generation models, and multimodal 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 Us at  https://huggingface.co/Tencent-Hunyuan/HunyuanDiT and https://github.com/Tencent/HunyuanDiT .
+k.	“Tencent Hunyuan Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof.
+l.	“Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You.
+m.	“including” shall mean including but not limited to.
+2.	GRANT OF RIGHTS.
+We grant You a non-exclusive, worldwide, non-transferable and royalty-free limited license under Tencent’s intellectual property or other rights owned by Us embodied in or utilized by the Materials to use, reproduce, distribute, create derivative works of (including Model Derivatives), and make modifications to the Materials, only in accordance with the terms of this Agreement and the Acceptable Use Policy, and You must not violate (or encourage or permit anyone else to violate) any term of this Agreement or the Acceptable Use Policy.
+3.	DISTRIBUTION.
+You may, subject to Your compliance with this Agreement, distribute or make available to Third Parties the Tencent Hunyuan Works, provided that You meet all of the following conditions:
+a.	You must provide all such Third Party recipients of the Tencent Hunyuan Works or products or services using them a copy of this Agreement;
+b.	You must cause any modified files to carry prominent notices stating that You changed the files;
+c.	You are encouraged to: (i) publish at least one technology introduction blogpost or one public statement expressing Your experience of using the Tencent Hunyuan Works; and (ii) mark the products or services developed by using the Tencent Hunyuan Works to indicate that the product/service is “Powered by Tencent Hunyuan”; and
+d.	All distributions to Third Parties (other than through a Hosted Service) must be accompanied by a “Notice” text file that contains the following notice: “Tencent Hunyuan is licensed under the Tencent Hunyuan Community License Agreement, Copyright © 2024 Tencent. All Rights Reserved. The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.”
+You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement. If You receive Tencent Hunyuan Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You.
+4.	ADDITIONAL COMMERCIAL TERMS.
+If, on the Tencent Hunyuan version release date, the monthly active users of all products or services made available by or for Licensee is greater than 100 million monthly active users in the preceding calendar month, You must request a license from Tencent, which Tencent may grant to You in its sole discretion, and You are not authorized to exercise any of the rights under this Agreement unless or until Tencent otherwise expressly grants You such rights.
+5.	RULES OF USE.
+a.	Your use of the Tencent Hunyuan Works must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Tencent Hunyuan Works, which is hereby incorporated by reference into this Agreement. You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan Works are subject to the use restrictions in these Sections 5(a) and 5(b).
+b.	You must not use the Tencent Hunyuan Works or any Output or results of the Tencent Hunyuan Works to improve any other large language model (other than Tencent Hunyuan or Model Derivatives thereof).
+6.	INTELLECTUAL PROPERTY.
+a.	Subject to Tencent’s ownership of Tencent Hunyuan Works made by or for Tencent and intellectual property rights therein, conditioned upon Your compliance with the terms and conditions of this Agreement, as between You and Tencent, You will be the owner of any derivative works and modifications of the Materials and any Model Derivatives that are made by or for You.
+b.	No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent.
+c.	If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan Works.
+d.	Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses.
+7.	DISCLAIMERS OF WARRANTY AND LIMITATIONS OF LIABILITY.
+a.	We are not obligated to support, update, provide training for, or develop any further version of the Tencent Hunyuan Works or to grant any license thereto.
+b.	UNLESS AND ONLY TO THE EXTENT REQUIRED BY APPLICABLE LAW, THE TENCENT HUNYUAN WORKS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED “AS IS” WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES OF ANY KIND INCLUDING ANY WARRANTIES OF TITLE, MERCHANTABILITY, NONINFRINGEMENT, COURSE OF DEALING, USAGE OF TRADE, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR OR A THIRD PARTY’S USE OR DISTRIBUTION OF ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT.
+c.	TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL TENCENT OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, FOR ANY DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS, EVEN IF TENCENT OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+8.	SURVIVAL AND TERMINATION.
+a.	The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
+b.	We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
+9.	GOVERNING LAW AND JURISDICTION.
+a.	This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+b.	Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
+ 
+
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: 2024/5/14
+
+Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan. You agree not to use Tencent Hunyuan or Model Derivatives:
+1.	In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
+2.	To harm Yourself or others;
+3.	To repurpose or distribute output from Tencent Hunyuan or any Model Derivatives to harm Yourself or others; 
+4.	To override or circumvent the safety guardrails and safeguards We have put in place;
+5.	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+6.	To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
+7.	To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
+8.	To intentionally defame, disparage or otherwise harass others;
+9.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+10.	To generate or disseminate personal identifiable information with the purpose of harming others;
+11.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+12.	To impersonate another individual without consent, authorization, or legal right;
+13.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+14.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+15.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+16.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+17.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+18.	For military purposes;
+19.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
+
+For the license of other third party components, please refer to the following URL:
+https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/Notice
+
+--------------------------------------------------------------------
+
+This Model also incorporates insights from Flux's neural network architechtures (https://github.com/black-forest-labs/flux?tab=readme-ov-file). Credits are given to the orginal authors.
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/__init__.py
new file mode 100644
index 0000000..e307c3f
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/__init__.py
@@ -0,0 +1,23 @@
+# 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
+# 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.
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/fix_configs.py b/Gen_3D_Modules/Hunyuan3D_V2/fix_configs.py
new file mode 100644
index 0000000..43f2bbc
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/fix_configs.py
@@ -0,0 +1,24 @@
+import os
+
+TARGET_DIR = "Checkpoints/Diffusers/tencent/Hunyuan3D-2"
+OLD_IMPORT = "hy3dgen"
+NEW_IMPORT = "Hunyuan3D_V2.hy3dgen"
+
+def patch_yaml_imports(root_dir):
+    for subdir, _, files in os.walk(root_dir):
+        for file in files:
+            if file.endswith(".yaml") or file.endswith(".yml"):
+                file_path = os.path.join(subdir, file)
+                with open(file_path, "r", encoding="utf-8") as f:
+                    content = f.read()
+
+                if OLD_IMPORT in content:
+                    new_content = content.replace(f"{OLD_IMPORT}.", f"{NEW_IMPORT}.")
+                    with open(file_path, "w", encoding="utf-8") as f:
+                        f.write(new_content)
+                    print(f"✔ Updated: {file_path}")
+                else:
+                    print(f"– Skipped (no match): {file_path}")
+
+if __name__ == "__main__":
+    patch_yaml_imports(TARGET_DIR)
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/__init__.py
index e307c3f..8bb2bf8 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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.
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/rembg.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/rembg.py
index c0d9948..6247f06 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/rembg.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
 
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/__init__.py
old mode 100644
new mode 100755
index d1f9534..1b1f9cc
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -23,5 +13,5 @@
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
 from .pipelines import Hunyuan3DDiTPipeline, Hunyuan3DDiTFlowMatchingPipeline
-from .postprocessors import FaceReducer, FloaterRemover, DegenerateFaceRemover
+from .postprocessors import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier
 from .preprocessors import ImageProcessorV2, IMAGE_PROCESSORS, DEFAULT_IMAGEPROCESSOR
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/__init__.py
old mode 100644
new mode 100755
index 684b3e3..8179353
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/__init__.py
@@ -23,6 +23,6 @@
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
 
+from .autoencoders import ShapeVAE
 from .conditioner import DualImageEncoder, SingleImageEncoder, DinoImageEncoder, CLIPImageEncoder
-from .hunyuan3ddit import Hunyuan3DDiT
-from .vae import ShapeVAE
+from .denoisers import Hunyuan3DDiT
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/__init__.py
new file mode 100644
index 0000000..20bbf8d
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/vae.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_blocks.py
similarity index 63%
rename from Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/vae.py
rename to Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_blocks.py
index aef2784..ab34eeb 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/vae.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_blocks.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
@@ -22,15 +12,25 @@
 # 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 Tuple, List, Union, Optional
 
-import numpy as np
+import os
+from typing import Optional
+
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from skimage import measure
-from tqdm import tqdm
+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):
@@ -166,13 +166,14 @@ 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 * 4)
-        self.c_proj = nn.Linear(width * 4, output_width if output_width is not None else 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()
 
@@ -196,6 +197,8 @@ class QKVMultiheadCrossAttention(nn.Module):
         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
@@ -206,10 +209,9 @@ class QKVMultiheadCrossAttention(nn.Module):
 
         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 = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
-
+        out = self.attn_processor(self, q, k, v)
+        out = out.transpose(1, 2).reshape(bs, n_ctx, -1)
         return out
 
 
@@ -223,7 +225,8 @@ class MultiheadCrossAttention(nn.Module):
         n_data: Optional[int] = None,
         data_width: Optional[int] = None,
         norm_layer=nn.LayerNorm,
-        qk_norm: bool = False
+        qk_norm: bool = False,
+        kv_cache: bool = False,
     ):
         super().__init__()
         self.n_data = n_data
@@ -240,10 +243,18 @@ class MultiheadCrossAttention(nn.Module):
             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)
-        data = self.c_kv(data)
+        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
@@ -256,6 +267,7 @@ class ResidualCrossAttentionBlock(nn.Module):
         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,
@@ -278,7 +290,7 @@ class ResidualCrossAttentionBlock(nn.Module):
         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)
+        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))
@@ -312,7 +324,7 @@ class QKVMultiheadAttention(nn.Module):
         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 = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
+        out = scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
         return out
 
 
@@ -430,207 +442,52 @@ class CrossAttentionDecoder(nn.Module):
         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
         )
 
-        self.ln_post = nn.LayerNorm(width)
+        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 forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
-        queries = self.query_proj(self.fourier_embedder(queries).to(latents.dtype))
-        x = self.cross_attn_decoder(queries, latents)
-        x = self.ln_post(x)
+    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
-
-
-def generate_dense_grid_points(bbox_min: np.ndarray,
-                               bbox_max: np.ndarray,
-                               octree_depth: int,
-                               indexing: str = "ij",
-                               octree_resolution: int = None,
-                               ):
-    length = bbox_max - bbox_min
-    num_cells = np.exp2(octree_depth)
-    if octree_resolution is not None:
-        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)
-    xyz = xyz.reshape(-1, 3)
-    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
-
-    return xyz, grid_size, length
-
-
-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 Latent2MeshOutput:
-
-    def __init__(self, mesh_v=None, mesh_f=None):
-        self.mesh_v = mesh_v
-        self.mesh_f = mesh_f
-
-
-class ShapeVAE(nn.Module):
-    def __init__(
-        self,
-        *,
-        num_latents: int,
-        embed_dim: int,
-        width: int,
-        heads: int,
-        num_decoder_layers: int,
-        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.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,
-            width=width,
-            heads=heads,
-            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
-
-    @torch.no_grad()
-    def latents2mesh(
-        self,
-        latents: torch.FloatTensor,
-        bounds: Union[Tuple[float], List[float], float] = 1.1,
-        octree_depth: int = 7,
-        num_chunks: int = 10000,
-        mc_level: float = -1 / 512,
-        octree_resolution: int = None,
-        mc_algo: str = 'dmc',
-    ):
-        device = latents.device
-
-        # 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_depth=octree_depth,
-            octree_resolution=octree_resolution,
-            indexing="ij"
-        )
-        xyz_samples = torch.FloatTensor(xyz_samples)
-
-        # 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"MC Level {mc_level} Implicit Function:"):
-            queries = xyz_samples[start: start + num_chunks, :].to(device)
-            queries = queries.half()
-            batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
-
-            logits = self.geo_decoder(batch_queries.to(latents.dtype), latents)
-            if mc_level == -1:
-                mc_level = 0
-                logits = torch.sigmoid(logits) * 2 - 1
-                print(f'Training with soft labels, inference with sigmoid and marching cubes level 0.')
-            batch_logits.append(logits)
-        grid_logits = torch.cat(batch_logits, dim=1)
-        grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float()
-
-        # 3. extract surface
-        outputs = []
-        for i in range(batch_size):
-            try:
-                if mc_algo == 'mc':
-                    vertices, faces, normals, _ = measure.marching_cubes(
-                        grid_logits[i].cpu().numpy(),
-                        mc_level,
-                        method="lewiner"
-                    )
-                    vertices = vertices / grid_size * bbox_size + bbox_min
-                elif mc_algo == 'dmc':
-                    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)
-                    octree_resolution = 2 ** octree_depth if octree_resolution is None else octree_resolution
-                    sdf = -grid_logits[i] / octree_resolution
-                    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]
-                else:
-                    raise ValueError(f"mc_algo {mc_algo} not supported.")
-
-                outputs.append(
-                    Latent2MeshOutput(
-                        mesh_v=vertices.astype(np.float32),
-                        mesh_f=np.ascontiguousarray(faces)
-                    )
-                )
-
-            except ValueError:
-                outputs.append(None)
-            except RuntimeError:
-                outputs.append(None)
-
-        return outputs
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_processors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/attention_processors.py
new file mode 100644
index 0000000..f7b232e
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/model.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/model.py
new file mode 100644
index 0000000..76f78da
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/surface_extractors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/surface_extractors.py
new file mode 100644
index 0000000..f4d8f63
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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,
+}
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/volume_decoders.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/autoencoders/volume_decoders.py
new file mode 100644
index 0000000..d7bfd84
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py
old mode 100644
new mode 100755
index 1af4c0c..d0d848c
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/conditioner.py
@@ -22,6 +22,7 @@
 # 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
 import torch
 import torch.nn as nn
 from torchvision import transforms
@@ -33,6 +34,26 @@ from transformers import (
 )
 
 
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000 ** omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    return np.concatenate([emb_sin, emb_cos], axis=1)
+
+
 class ImageEncoder(nn.Module):
     def __init__(
         self,
@@ -67,7 +88,7 @@ class ImageEncoder(nn.Module):
             ]
         )
 
-    def forward(self, image, mask=None, value_range=(-1, 1)):
+    def forward(self, image, mask=None, value_range=(-1, 1), **kwargs):
         if value_range is not None:
             low, high = value_range
             image = (image - low) / (high - low)
@@ -82,7 +103,7 @@ class ImageEncoder(nn.Module):
 
         return last_hidden_state
 
-    def unconditional_embedding(self, batch_size):
+    def unconditional_embedding(self, batch_size, **kwargs):
         device = next(self.model.parameters()).device
         dtype = next(self.model.parameters()).dtype
         zero = torch.zeros(
@@ -110,11 +131,82 @@ class DinoImageEncoder(ImageEncoder):
     std = [0.229, 0.224, 0.225]
 
 
+class DinoImageEncoderMV(DinoImageEncoder):
+    def __init__(
+        self,
+        version=None,
+        config=None,
+        use_cls_token=True,
+        image_size=224,
+        view_num=4,
+        **kwargs,
+    ):
+        super().__init__(version, config, use_cls_token, image_size, **kwargs)
+        self.view_num = view_num
+        self.num_patches = self.num_patches
+        pos = np.arange(self.view_num, dtype=np.float32)
+        view_embedding = torch.from_numpy(
+            get_1d_sincos_pos_embed_from_grid(self.model.config.hidden_size, pos)).float()
+
+        view_embedding = view_embedding.unsqueeze(1).repeat(1, self.num_patches, 1)
+        self.view_embed = view_embedding.unsqueeze(0)
+
+    def forward(self, image, mask=None, value_range=(-1, 1), view_idxs=None):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.model.device, dtype=self.model.dtype)
+
+        bs, num_views, c, h, w = image.shape
+        image = image.view(bs * num_views, c, h, w)
+
+        inputs = self.transform(image)
+        outputs = self.model(inputs)
+
+        last_hidden_state = outputs.last_hidden_state
+        last_hidden_state = last_hidden_state.view(
+            bs, num_views, last_hidden_state.shape[-2],
+            last_hidden_state.shape[-1]
+        )
+
+        view_embedding = self.view_embed.to(last_hidden_state.dtype).to(last_hidden_state.device)
+        if view_idxs is not None:
+            assert len(view_idxs) == bs
+            view_embeddings = []
+            for i in range(bs):
+                view_idx = view_idxs[i]
+                assert num_views == len(view_idx)
+                view_embeddings.append(self.view_embed[:, view_idx, ...])
+            view_embedding = torch.cat(view_embeddings, 0).to(last_hidden_state.dtype).to(last_hidden_state.device)
+
+        if num_views != self.view_num:
+            view_embedding = view_embedding[:, :num_views, ...]
+        last_hidden_state = last_hidden_state + view_embedding
+        last_hidden_state = last_hidden_state.view(bs, num_views * last_hidden_state.shape[-2],
+                                                   last_hidden_state.shape[-1])
+        return last_hidden_state
+
+    def unconditional_embedding(self, batch_size, view_idxs=None, **kwargs):
+        device = next(self.model.parameters()).device
+        dtype = next(self.model.parameters()).dtype
+        zero = torch.zeros(
+            batch_size,
+            self.num_patches * len(view_idxs[0]),
+            self.model.config.hidden_size,
+            device=device,
+            dtype=dtype,
+        )
+        return zero
+
+
 def build_image_encoder(config):
     if config['type'] == 'CLIPImageEncoder':
         return CLIPImageEncoder(**config['kwargs'])
     elif config['type'] == 'DinoImageEncoder':
         return DinoImageEncoder(**config['kwargs'])
+    elif config['type'] == 'DinoImageEncoderMV':
+        return DinoImageEncoderMV(**config['kwargs'])
     else:
         raise ValueError(f'Unknown image encoder type: {config["type"]}')
 
@@ -129,17 +221,17 @@ class DualImageEncoder(nn.Module):
         self.main_image_encoder = build_image_encoder(main_image_encoder)
         self.additional_image_encoder = build_image_encoder(additional_image_encoder)
 
-    def forward(self, image, mask=None):
+    def forward(self, image, mask=None, **kwargs):
         outputs = {
-            'main': self.main_image_encoder(image, mask=mask),
-            'additional': self.additional_image_encoder(image, mask=mask),
+            'main': self.main_image_encoder(image, mask=mask, **kwargs),
+            'additional': self.additional_image_encoder(image, mask=mask, **kwargs),
         }
         return outputs
 
-    def unconditional_embedding(self, batch_size):
+    def unconditional_embedding(self, batch_size, **kwargs):
         outputs = {
-            'main': self.main_image_encoder.unconditional_embedding(batch_size),
-            'additional': self.additional_image_encoder.unconditional_embedding(batch_size),
+            'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs),
+            'additional': self.additional_image_encoder.unconditional_embedding(batch_size, **kwargs),
         }
         return outputs
 
@@ -152,14 +244,14 @@ class SingleImageEncoder(nn.Module):
         super().__init__()
         self.main_image_encoder = build_image_encoder(main_image_encoder)
 
-    def forward(self, image, mask=None):
+    def forward(self, image, mask=None, **kwargs):
         outputs = {
-            'main': self.main_image_encoder(image, mask=mask),
+            'main': self.main_image_encoder(image, mask=mask, **kwargs),
         }
         return outputs
 
-    def unconditional_embedding(self, batch_size):
+    def unconditional_embedding(self, batch_size, **kwargs):
         outputs = {
-            'main': self.main_image_encoder.unconditional_embedding(batch_size),
+            'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs),
         }
         return outputs
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/__init__.py
new file mode 100644
index 0000000..7260933
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/__init__.py
@@ -0,0 +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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/hunyuan3ddit.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/hunyuan3ddit.py
old mode 100644
new mode 100755
similarity index 91%
rename from Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/hunyuan3ddit.py
rename to Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/denoisers/hunyuan3ddit.py
index d1c7786..7873f16
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/models/hunyuan3ddit.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -23,6 +13,7 @@
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
 import math
+import os
 from dataclasses import dataclass
 from typing import List, Tuple, Optional
 
@@ -30,9 +21,17 @@ import torch
 from einops import rearrange
 from torch import Tensor, nn
 
+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
+
 
 def attention(q: Tensor, k: Tensor, v: Tensor, **kwargs) -> Tensor:
-    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = scaled_dot_product_attention(q, k, v)
     x = rearrange(x, "B H L D -> B L (H D)")
     return x
 
@@ -61,6 +60,15 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10
     return embedding
 
 
+class GELU(nn.Module):
+    def __init__(self, approximate='tanh'):
+        super().__init__()
+        self.approximate = approximate
+
+    def forward(self, x: Tensor) -> Tensor:
+        return nn.functional.gelu(x.contiguous(), approximate=self.approximate)
+
+
 class MLPEmbedder(nn.Module):
     def __init__(self, in_dim: int, hidden_dim: int):
         super().__init__()
@@ -163,7 +171,7 @@ class DoubleStreamBlock(nn.Module):
         self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.img_mlp = nn.Sequential(
             nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
+            GELU(approximate="tanh"),
             nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
         )
 
@@ -174,7 +182,7 @@ class DoubleStreamBlock(nn.Module):
         self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.txt_mlp = nn.Sequential(
             nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
+            GELU(approximate="tanh"),
             nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
         )
 
@@ -240,7 +248,7 @@ class SingleStreamBlock(nn.Module):
         self.hidden_size = hidden_size
         self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
 
-        self.mlp_act = nn.GELU(approximate="tanh")
+        self.mlp_act = GELU(approximate="tanh")
         self.modulation = Modulation(hidden_size, double=False)
 
     def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
@@ -287,6 +295,7 @@ class Hunyuan3DDiT(nn.Module):
         theta: int = 10_000,
         qkv_bias: bool = True,
         time_factor: float = 1000,
+        guidance_embed: bool = False,
         ckpt_path: Optional[str] = None,
         **kwargs,
     ):
@@ -303,6 +312,7 @@ class Hunyuan3DDiT(nn.Module):
         self.qkv_bias = qkv_bias
         self.time_factor = time_factor
         self.out_channels = self.in_channels
+        self.guidance_embed = guidance_embed
 
         if hidden_size % num_heads != 0:
             raise ValueError(
@@ -316,6 +326,9 @@ class Hunyuan3DDiT(nn.Module):
         self.latent_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
         self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
         self.cond_in = nn.Linear(context_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if guidance_embed else nn.Identity()
+        )
 
         self.double_blocks = nn.ModuleList(
             [
@@ -374,7 +387,14 @@ class Hunyuan3DDiT(nn.Module):
     ) -> Tensor:
         cond = contexts['main']
         latent = self.latent_in(x)
+
         vec = self.time_in(timestep_embedding(t, 256, self.time_factor).to(dtype=latent.dtype))
+        if self.guidance_embed:
+            guidance = kwargs.get('guidance', None)
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.time_factor))
+
         cond = self.cond_in(cond)
         pe = None
 
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py
old mode 100644
new mode 100755
index 8c13fda..82b4461
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/pipelines.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -25,7 +15,6 @@
 import copy
 import importlib
 import inspect
-import logging
 import os
 from typing import List, Optional, Union
 
@@ -35,11 +24,12 @@ import trimesh
 import yaml
 from PIL import Image
 from diffusers.utils.torch_utils import randn_tensor
+from diffusers.utils.import_utils import is_accelerate_version, is_accelerate_available
 from tqdm import tqdm
 
-import comfy.utils
-
-logger = logging.getLogger(__name__)
+from .models.autoencoders import ShapeVAE
+from .models.autoencoders import SurfaceExtractors
+from .utils import logger, synchronize_timer, smart_load_model
 
 
 def retrieve_timesteps(
@@ -101,6 +91,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
+@synchronize_timer('Export to trimesh')
 def export_to_trimesh(mesh_output):
     if isinstance(mesh_output, list):
         outputs = []
@@ -137,7 +128,11 @@ def instantiate_from_config(config, **kwargs):
 
 
 class Hunyuan3DDiTPipeline:
+    model_cpu_offload_seq = "conditioner->model->vae"
+    _exclude_from_cpu_offload = []
+
     @classmethod
+    @synchronize_timer('Hunyuan3DDiTPipeline Model Loading')
     def from_single_file(
         cls,
         ckpt_path,
@@ -156,7 +151,7 @@ class Hunyuan3DDiTPipeline:
             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}")
+        # logger.info(f"Loading model from {ckpt_path}")
 
         if use_safetensors:
             # parse safetensors
@@ -170,7 +165,7 @@ class Hunyuan3DDiTPipeline:
                     ckpt[model_name] = {}
                 ckpt[model_name][new_key] = value
         else:
-            ckpt = torch.load(ckpt_path, map_location='cpu')
+            ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True)
         # load model
         model = instantiate_from_config(config['model'])
         model.load_state_dict(ckpt['model'])
@@ -201,21 +196,27 @@ class Hunyuan3DDiTPipeline:
     def from_pretrained(
         cls,
         model_path,
-        ckpt_name='model.ckpt',
-        config_name='config.yaml',
         device='cuda',
         dtype=torch.float16,
-        use_safetensors=None,
+        use_safetensors=True,
+        variant='fp16',
+        subfolder='hunyuan3d-dit-v2-0',
         **kwargs,
     ):
-        model_path = os.path.join(model_path, 'hunyuan3d-dit-v2-0')
-
-        config_path = os.path.join(model_path, config_name)
-        ckpt_path = os.path.join(model_path, ckpt_name)
-
-        if not os.path.exists(model_path):
-            raise FileNotFoundError(f"Model path {model_path} not found")
-
+        kwargs['from_pretrained_kwargs'] = dict(
+            model_path=model_path,
+            subfolder=subfolder,
+            use_safetensors=use_safetensors,
+            variant=variant,
+            dtype=dtype,
+            device=device,
+        )
+        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,
@@ -241,29 +242,187 @@ class Hunyuan3DDiTPipeline:
         self.scheduler = scheduler
         self.conditioner = conditioner
         self.image_processor = image_processor
-
+        self.kwargs = kwargs
         self.to(device, dtype)
 
+    def compile(self):
+        self.vae = torch.compile(self.vae)
+        self.model = torch.compile(self.model)
+        self.conditioner = torch.compile(self.conditioner)
+
+    def enable_flashvdm(
+        self,
+        enabled: bool = True,
+        adaptive_kv_selection=True,
+        topk_mode='mean',
+        mc_algo='mc',
+        replace_vae=True,
+    ):
+        if enabled:
+            model_path = self.kwargs['from_pretrained_kwargs']['model_path']
+            turbo_vae_mapping = {
+                'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'),
+                'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'),
+                'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini-turbo'),
+            }
+            model_name = model_path.split('/')[-1]
+            if replace_vae and model_name in turbo_vae_mapping:
+                model_path, subfolder = turbo_vae_mapping[model_name]
+                self.vae = ShapeVAE.from_pretrained(
+                    model_path, subfolder=subfolder,
+                    use_safetensors=self.kwargs['from_pretrained_kwargs']['use_safetensors'],
+                    device=self.device,
+                )
+            self.vae.enable_flashvdm_decoder(
+                enabled=enabled,
+                adaptive_kv_selection=adaptive_kv_selection,
+                topk_mode=topk_mode,
+                mc_algo=mc_algo
+            )
+        else:
+            model_path = self.kwargs['from_pretrained_kwargs']['model_path']
+            vae_mapping = {
+                'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'),
+                'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'),
+                'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini'),
+            }
+            model_name = model_path.split('/')[-1]
+            if model_name in vae_mapping:
+                model_path, subfolder = vae_mapping[model_name]
+                self.vae = ShapeVAE.from_pretrained(model_path, subfolder=subfolder)
+            self.vae.enable_flashvdm_decoder(enabled=False)
+
     def to(self, device=None, dtype=None):
-        if device is not None:
-            self.device = torch.device(device)
-            self.vae.to(device)
-            self.model.to(device)
-            self.conditioner.to(device)
         if dtype is not None:
             self.dtype = dtype
             self.vae.to(dtype=dtype)
             self.model.to(dtype=dtype)
             self.conditioner.to(dtype=dtype)
-            
+        if device is not None:
+            self.device = torch.device(device)
+            self.vae.to(device)
+            self.model.to(device)
+            self.conditioner.to(device)
         return self
 
-    def encode_cond(self, image, mask, do_classifier_free_guidance, dual_guidance):
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from
+        Accelerate's module hooks.
+        """
+        for name, model in self.components.items():
+            if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload:
+                continue
+
+            if not hasattr(model, "_hf_hook"):
+                return self.device
+            for module in model.modules():
+                if (
+                    hasattr(module, "_hf_hook")
+                    and hasattr(module._hf_hook, "execution_device")
+                    and module._hf_hook.execution_device is not None
+                ):
+                    return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
+        r"""
+        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+
+        Arguments:
+            gpu_id (`int`, *optional*):
+                The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.
+            device (`torch.Device` or `str`, *optional*, defaults to "cuda"):
+                The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will
+                default to "cuda".
+        """
+        if self.model_cpu_offload_seq is None:
+            raise ValueError(
+                "Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set."
+            )
+
+        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+            from accelerate import cpu_offload_with_hook
+        else:
+            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+        torch_device = torch.device(device)
+        device_index = torch_device.index
+
+        if gpu_id is not None and device_index is not None:
+            raise ValueError(
+                f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}"
+                f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}"
+            )
+
+        # _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0
+        self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, "_offload_gpu_id", 0)
+
+        device_type = torch_device.type
+        device = torch.device(f"{device_type}:{self._offload_gpu_id}")
+
+        if self.device.type != "cpu":
+            self.to("cpu")
+            device_mod = getattr(torch, self.device.type, None)
+            if hasattr(device_mod, "empty_cache") and device_mod.is_available():
+                device_mod.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
+
+        all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}
+
+        self._all_hooks = []
+        hook = None
+        for model_str in self.model_cpu_offload_seq.split("->"):
+            model = all_model_components.pop(model_str, None)
+            if not isinstance(model, torch.nn.Module):
+                continue
+
+            _, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook)
+            self._all_hooks.append(hook)
+
+        # CPU offload models that are not in the seq chain unless they are explicitly excluded
+        # these models will stay on CPU until maybe_free_model_hooks is called
+        # some models cannot be in the seq chain because they are iteratively called, such as controlnet
+        for name, model in all_model_components.items():
+            if not isinstance(model, torch.nn.Module):
+                continue
+
+            if name in self._exclude_from_cpu_offload:
+                model.to(device)
+            else:
+                _, hook = cpu_offload_with_hook(model, device)
+                self._all_hooks.append(hook)
+
+    def maybe_free_model_hooks(self):
+        r"""
+        Function that offloads all components, removes all model hooks that were added when using
+        `enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function
+        is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it
+        functions correctly when applying enable_model_cpu_offload.
+        """
+        if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0:
+            # `enable_model_cpu_offload` has not be called, so silently do nothing
+            return
+
+        for hook in self._all_hooks:
+            # offload model and remove hook from model
+            hook.offload()
+            hook.remove()
+
+        # make sure the model is in the same state as before calling it
+        self.enable_model_cpu_offload()
+
+    @synchronize_timer('Encode cond')
+    def encode_cond(self, image, additional_cond_inputs, do_classifier_free_guidance, dual_guidance):
         bsz = image.shape[0]
-        cond = self.conditioner(image=image, mask=mask)
+        cond = self.conditioner(image=image, **additional_cond_inputs)
 
         if do_classifier_free_guidance:
-            un_cond = self.conditioner.unconditional_embedding(bsz)
+            un_cond = self.conditioner.unconditional_embedding(bsz, **additional_cond_inputs)
 
             if dual_guidance:
                 un_cond_drop_main = copy.deepcopy(un_cond)
@@ -279,8 +438,6 @@ class Hunyuan3DDiTPipeline:
 
                 cond = cat_recursive(cond, un_cond_drop_main, un_cond)
             else:
-                un_cond = self.conditioner.unconditional_embedding(bsz)
-
                 def cat_recursive(a, b):
                     if isinstance(a, torch.Tensor):
                         return torch.cat([a, b], dim=0).to(self.dtype)
@@ -326,25 +483,27 @@ class Hunyuan3DDiTPipeline:
         latents = latents * getattr(self.scheduler, 'init_noise_sigma', 1.0)
         return latents
 
-    def prepare_image(self, image):
+    def prepare_image(self, image) -> dict:
         if isinstance(image, str) and not os.path.exists(image):
             raise FileNotFoundError(f"Couldn't find image at path {image}")
 
         if not isinstance(image, list):
             image = [image]
-        image_pts = []
-        mask_pts = []
-        for img in image:
-            image_pt, mask_pt = self.image_processor(img, return_mask=True)
-            image_pts.append(image_pt)
-            mask_pts.append(mask_pt)
 
-        image_pts = torch.cat(image_pts, dim=0).to(self.device, dtype=self.dtype)
-        if mask_pts[0] is not None:
-            mask_pts = torch.cat(mask_pts, dim=0).to(self.device, dtype=self.dtype)
-        else:
-            mask_pts = None
-        return image_pts, mask_pts
+        outputs = []
+        for img in image:
+            output = self.image_processor(img)
+            outputs.append(output)
+
+        cond_input = {k: [] for k in outputs[0].keys()}
+        for output in outputs:
+            for key, value in output.items():
+                cond_input[key].append(value)
+        for key, value in cond_input.items():
+            if isinstance(value[0], torch.Tensor):
+                cond_input[key] = torch.cat(value, dim=0)
+
+        return cond_input
 
     def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
         """
@@ -374,6 +533,17 @@ class Hunyuan3DDiTPipeline:
         assert emb.shape == (w.shape[0], embedding_dim)
         return emb
 
+    def set_surface_extractor(self, mc_algo):
+        if mc_algo is None:
+            return
+        logger.info('The parameters `mc_algo` is deprecated, and will be removed in future versions.\n'
+                    'Please use: \n'
+                    'from hy3dgen.shapegen.models.autoencoders import SurfaceExtractors\n'
+                    'pipeline.vae.surface_extractor = SurfaceExtractors[mc_algo]() instead\n')
+        if mc_algo not in SurfaceExtractors.keys():
+            raise ValueError(f"Unknown mc_algo {mc_algo}")
+        self.vae.surface_extractor = SurfaceExtractors[mc_algo]()
+
     @torch.no_grad()
     def __call__(
         self,
@@ -390,7 +560,7 @@ class Hunyuan3DDiTPipeline:
         octree_resolution=384,
         mc_level=-1 / 512,
         num_chunks=8000,
-        mc_algo='mc',
+        mc_algo=None,
         output_type: Optional[str] = "trimesh",
         enable_pbar=True,
         **kwargs,
@@ -398,17 +568,22 @@ class Hunyuan3DDiTPipeline:
         callback = kwargs.pop("callback", None)
         callback_steps = kwargs.pop("callback_steps", None)
 
+        self.set_surface_extractor(mc_algo)
+
         device = self.device
         dtype = self.dtype
         do_classifier_free_guidance = guidance_scale >= 0 and \
                                       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,
-                                mask=mask,
-                                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
@@ -420,46 +595,45 @@ class Hunyuan3DDiTPipeline:
 
         guidance_cond = None
         if getattr(self.model, 'guidance_cond_proj_dim', None) is not None:
-            print('Using lcm guidance scale')
+            logger.info('Using lcm guidance scale')
             guidance_scale_tensor = torch.tensor(guidance_scale - 1).repeat(batch_size)
             guidance_cond = self.get_guidance_scale_embedding(
                 guidance_scale_tensor, embedding_dim=self.model.guidance_cond_proj_dim
             ).to(device=device, dtype=latents.dtype)
-            
-        
-        for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)):
-            # expand the latents if we are doing classifier free guidance
-            if do_classifier_free_guidance:
-                latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2))
-            else:
-                latent_model_input = latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device)
-            timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0])
-            noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond)
-
-            # no drop, drop clip, all drop
-            if do_classifier_free_guidance:
-                if dual_guidance:
-                    noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3)
-                    noise_pred = (
-                        noise_pred_uncond
-                        + guidance_scale * (noise_pred_clip - noise_pred_dino)
-                        + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond)
-                    )
+        with synchronize_timer('Diffusion Sampling'):
+            for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)):
+                # expand the latents if we are doing classifier free guidance
+                if do_classifier_free_guidance:
+                    latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2))
                 else:
-                    noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+                    latent_model_input = latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
-            # compute the previous noisy sample x_t -> x_t-1
-            outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
-            latents = outputs.prev_sample
+                # predict the noise residual
+                timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device)
+                timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0])
+                noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond)
 
-            if callback is not None and i % callback_steps == 0:
-                step_idx = i // getattr(self.scheduler, "order", 1)
-                callback(step_idx, t, outputs)
+                # no drop, drop clip, all drop
+                if do_classifier_free_guidance:
+                    if dual_guidance:
+                        noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3)
+                        noise_pred = (
+                            noise_pred_uncond
+                            + guidance_scale * (noise_pred_clip - noise_pred_dino)
+                            + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond)
+                        )
+                    else:
+                        noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+                latents = outputs.prev_sample
+
+                if callback is not None and i % callback_steps == 0:
+                    step_idx = i // getattr(self.scheduler, "order", 1)
+                    callback(step_idx, t, outputs)
 
         return self._export(
             latents,
@@ -467,7 +641,17 @@ class Hunyuan3DDiTPipeline:
             box_v, mc_level, num_chunks, octree_resolution, mc_algo,
         )
 
-    def _export(self, latents, output_type, box_v, mc_level, num_chunks, octree_resolution, mc_algo):
+    def _export(
+        self,
+        latents,
+        output_type='trimesh',
+        box_v=1.01,
+        mc_level=0.0,
+        num_chunks=20000,
+        octree_resolution=256,
+        mc_algo='mc',
+        enable_pbar=True
+    ):
         if not output_type == "latent":
             latents = 1. / self.vae.scale_factor * latents
             latents = self.vae(latents)
@@ -478,6 +662,7 @@ class Hunyuan3DDiTPipeline:
                 num_chunks=num_chunks,
                 octree_resolution=octree_resolution,
                 mc_algo=mc_algo,
+                enable_pbar=enable_pbar,
             )
         else:
             outputs = latents
@@ -490,20 +675,20 @@ class Hunyuan3DDiTPipeline:
 
 class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline):
 
-    @torch.no_grad()
+    @torch.inference_mode()
     def __call__(
         self,
-        image: Union[str, List[str], Image.Image] = None,
+        image: Union[str, List[str], Image.Image, dict, List[dict]] = None,
         num_inference_steps: int = 50,
         timesteps: List[int] = None,
         sigmas: List[float] = None,
         eta: float = 0.0,
-        guidance_scale: float = 7.5,
+        guidance_scale: float = 5.0,
         generator=None,
         box_v=1.01,
         octree_resolution=384,
         mc_level=0.0,
-        mc_algo='mc',
+        mc_algo=None,
         num_chunks=8000,
         output_type: Optional[str] = "trimesh",
         enable_pbar=True,
@@ -512,6 +697,8 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline):
         callback = kwargs.pop("callback", None)
         callback_steps = kwargs.pop("callback_steps", None)
 
+        self.set_surface_extractor(mc_algo)
+
         device = self.device
         dtype = self.dtype
         do_classifier_free_guidance = guidance_scale >= 0 and not (
@@ -519,10 +706,11 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline):
             self.model.guidance_embed is True
         )
 
-        image, mask = self.prepare_image(image)
+        cond_inputs = self.prepare_image(image)
+        image = cond_inputs.pop('image')
         cond = self.encode_cond(
             image=image,
-            mask=mask,
+            additional_cond_inputs=cond_inputs,
             do_classifier_free_guidance=do_classifier_free_guidance,
             dual_guidance=False,
         )
@@ -543,36 +731,36 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline):
         if hasattr(self.model, 'guidance_embed') and \
             self.model.guidance_embed is True:
             guidance = torch.tensor([guidance_scale] * batch_size, device=device, dtype=dtype)
+            # logger.info(f'Using guidance embed with scale {guidance_scale}')
 
-        comfy_pbar = comfy.utils.ProgressBar(num_inference_steps)
-        for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")):
-            # expand the latents if we are doing classifier free guidance
-            if do_classifier_free_guidance:
-                latent_model_input = torch.cat([latents] * 2)
-            else:
-                latent_model_input = latents
+        with synchronize_timer('Diffusion Sampling'):
+            for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")):
+                # expand the latents if we are doing classifier free guidance
+                if do_classifier_free_guidance:
+                    latent_model_input = torch.cat([latents] * 2)
+                else:
+                    latent_model_input = latents
 
-            # NOTE: we assume model get timesteps ranged from 0 to 1
-            timestep = t.expand(latent_model_input.shape[0]).to(
-                latents.dtype) / self.scheduler.config.num_train_timesteps
-            noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance)
+                # NOTE: we assume model get timesteps ranged from 0 to 1
+                timestep = t.expand(latent_model_input.shape[0]).to(
+                    latents.dtype) / self.scheduler.config.num_train_timesteps
+                noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance)
 
-            if do_classifier_free_guidance:
-                noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+                if do_classifier_free_guidance:
+                    noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
 
-            # compute the previous noisy sample x_t -> x_t-1
-            outputs = self.scheduler.step(noise_pred, t, latents)
-            latents = outputs.prev_sample
+                # compute the previous noisy sample x_t -> x_t-1
+                outputs = self.scheduler.step(noise_pred, t, latents)
+                latents = outputs.prev_sample
 
-            if callback is not None and i % callback_steps == 0:
-                step_idx = i // getattr(self.scheduler, "order", 1)
-                callback(step_idx, t, outputs)
-
-            comfy_pbar.update_absolute(i + 1)
+                if callback is not None and i % callback_steps == 0:
+                    step_idx = i // getattr(self.scheduler, "order", 1)
+                    callback(step_idx, t, outputs)
 
         return self._export(
             latents,
             output_type,
             box_v, mc_level, num_chunks, octree_resolution, mc_algo,
+            enable_pbar=enable_pbar,
         )
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py
old mode 100644
new mode 100755
index 0500fa2..d258369
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/postprocessors.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -26,10 +16,13 @@ import os
 import tempfile
 from typing import Union
 
+import numpy as np
 import pymeshlab
+import torch
 import trimesh
 
-from .models.vae import Latent2MeshOutput
+from .models.autoencoders import Latent2MeshOutput
+from .utils import synchronize_timer
 
 
 def load_mesh(path):
@@ -42,6 +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,
@@ -64,15 +60,9 @@ def remove_floater(mesh: pymeshlab.MeshSet):
 
 
 def pymeshlab2trimesh(mesh: pymeshlab.MeshSet):
-    temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True)
-    temp_file.close()
-    temp_file_name = temp_file.name
-    
-    mesh.save_current_mesh(temp_file_name)
-    mesh = trimesh.load(temp_file_name)
-    if os.path.exists(temp_file_name):
-        os.remove(temp_file_name)
-          
+    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
     if isinstance(mesh, trimesh.Scene):
         combined_mesh = trimesh.Trimesh()
@@ -84,23 +74,17 @@ def pymeshlab2trimesh(mesh: pymeshlab.MeshSet):
 
 
 def trimesh2pymeshlab(mesh: trimesh.Trimesh):
-    temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True)
-    temp_file.close()
-    temp_file_name = temp_file.name
-    
-    if isinstance(mesh, trimesh.scene.Scene):
-        for idx, obj in enumerate(mesh.geometry.values()):
-            if idx == 0:
-                temp_mesh = obj
-            else:
-                temp_mesh = temp_mesh + obj
-        mesh = temp_mesh
-    mesh.export(temp_file_name)
-    mesh = pymeshlab.MeshSet()
-    mesh.load_new_mesh(temp_file_name)
-    if os.path.exists(temp_file_name):
-        os.remove(temp_file_name)
-          
+    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:
+                    temp_mesh = obj
+                else:
+                    temp_mesh = temp_mesh + obj
+            mesh = temp_mesh
+        mesh.export(temp_file.name)
+        mesh = pymeshlab.MeshSet()
+        mesh.load_new_mesh(temp_file.name)
     return mesh
 
 
@@ -132,6 +116,7 @@ def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutpu
 
 
 class FaceReducer:
+    @synchronize_timer('FaceReducer')
     def __call__(
         self,
         mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
@@ -144,6 +129,7 @@ class FaceReducer:
 
 
 class FloaterRemover:
+    @synchronize_timer('FloaterRemover')
     def __call__(
         self,
         mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
@@ -155,21 +141,62 @@ class FloaterRemover:
 
 
 class DegenerateFaceRemover:
+    @synchronize_timer('DegenerateFaceRemover')
     def __call__(
         self,
         mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
     ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]:
         ms = import_mesh(mesh)
 
-        temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True)
-        temp_file.close()
-        temp_file_name = temp_file.name
+        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)
 
-        ms.save_current_mesh(temp_file_name)
-        ms = pymeshlab.MeshSet()
-        ms.load_new_mesh(temp_file_name)
-        if os.path.exists(temp_file_name):
-            os.remove(temp_file_name)
-               
         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
+
+    @synchronize_timer('MeshSimplifier')
+    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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/preprocessors.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/preprocessors.py
old mode 100644
new mode 100755
index 2bdaff2..8a9cb9e
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/preprocessors.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -87,7 +78,7 @@ class ImageProcessorV2:
                                                           interpolation=cv2.INTER_AREA)
 
         bg = np.ones((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255
-        # bg = np.zeros((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255
+
         mask = result[..., 3:].astype(np.float32) / 255
         result = result[..., :3] * mask + bg * (1 - mask)
 
@@ -96,15 +87,13 @@ class ImageProcessorV2:
         mask = mask.clip(0, 255).astype(np.uint8)
         return result, mask
 
-    def __call__(self, image, border_ratio=0.15, to_tensor=True, return_mask=False, **kwargs):
-        if self.border_ratio is not None:
-            border_ratio = self.border_ratio
-            print(f"Using border_ratio from init: {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)
             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)
 
@@ -115,13 +104,64 @@ class ImageProcessorV2:
         if to_tensor:
             image = array_to_tensor(image)
             mask = array_to_tensor(mask)
-        if return_mask:
-            return image, mask
-        return image
+        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
+        }
+        return outputs
+
+
+class MVImageProcessorV2(ImageProcessorV2):
+    """
+    view order: front, front clockwise 90, back, front clockwise 270
+    """
+    return_view_idx = True
+
+    def __init__(self, size=512, border_ratio=None):
+        super().__init__(size, border_ratio)
+        self.view2idx = {
+            'front': 0,
+            'left': 1,
+            'back': 2,
+            'right': 3
+        }
+
+    def __call__(self, image_dict, border_ratio=0.15, to_tensor=True, **kwargs):
+        if self.border_ratio is not None:
+            border_ratio = self.border_ratio
+
+        images = []
+        masks = []
+        view_idxs = []
+        for idx, (view_tag, image) in enumerate(image_dict.items()):
+            view_idxs.append(self.view2idx[view_tag])
+            image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor)
+            images.append(image)
+            masks.append(mask)
+
+        zipped_lists = zip(view_idxs, images, masks)
+        sorted_zipped_lists = sorted(zipped_lists)
+        view_idxs, images, masks = zip(*sorted_zipped_lists)
+
+        image = torch.cat(images, 0).unsqueeze(0)
+        mask = torch.cat(masks, 0).unsqueeze(0)
+        outputs = {
+            'image': image,
+            'mask': mask,
+            'view_idxs': view_idxs
+        }
+        return outputs
 
 
 IMAGE_PROCESSORS = {
     "v2": ImageProcessorV2,
+    'mv_v2': MVImageProcessorV2,
 }
 
 DEFAULT_IMAGEPROCESSOR = 'v2'
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py
old mode 100644
new mode 100755
index 0069f5c..13f0da8
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/schedulers.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -305,3 +319,162 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
 
     def __len__(self):
         return self.config.num_train_timesteps
+
+
+@dataclass
+class ConsistencyFlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
+    prev_sample: torch.FloatTensor
+    pred_original_sample: torch.FloatTensor
+
+
+class ConsistencyFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        pcm_timesteps: int = 50,
+    ):
+        sigmas = np.linspace(0, 1, num_train_timesteps)
+        step_ratio = num_train_timesteps // pcm_timesteps
+
+        euler_timesteps = (np.arange(1, pcm_timesteps) * step_ratio).round().astype(np.int64) - 1
+        euler_timesteps = np.asarray([0] + euler_timesteps.tolist())
+
+        self.euler_timesteps = euler_timesteps
+        self.sigmas = sigmas[self.euler_timesteps]
+        self.sigmas = torch.from_numpy((self.sigmas.copy())).to(dtype=torch.float32)
+        self.timesteps = self.sigmas * num_train_timesteps
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def _sigma_to_t(self, sigma):
+        return sigma * self.config.num_train_timesteps
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+        self.num_inference_steps = num_inference_steps if num_inference_steps is not None else len(sigmas)
+        inference_indices = np.linspace(
+            0, self.config.pcm_timesteps, num=self.num_inference_steps, endpoint=False
+        )
+        inference_indices = np.floor(inference_indices).astype(np.int64)
+        inference_indices = torch.from_numpy(inference_indices).long()
+
+        self.sigmas_ = self.sigmas[inference_indices]
+        timesteps = self.sigmas_ * self.config.num_train_timesteps
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas_ = torch.cat(
+            [self.sigmas_, torch.ones(1, device=self.sigmas_.device)]
+        )
+
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[ConsistencyFlowMatchEulerDiscreteSchedulerOutput, Tuple]:
+        if (
+            isinstance(timestep, int)
+            or isinstance(timestep, torch.IntTensor)
+            or isinstance(timestep, torch.LongTensor)
+        ):
+            raise ValueError(
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas_[self.step_index]
+        sigma_next = self.sigmas_[self.step_index + 1]
+
+        prev_sample = sample + (sigma_next - sigma) * model_output
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        pred_original_sample = sample + (1.0 - sigma) * model_output
+        pred_original_sample = pred_original_sample.to(model_output.dtype)
+
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return ConsistencyFlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample,
+                                                                pred_original_sample=pred_original_sample)
+
+    def __len__(self):
+        return self.config.num_train_timesteps
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/utils.py
new file mode 100644
index 0000000..6ac8f5d
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/shapegen/utils.py
@@ -0,0 +1,126 @@
+# 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:
+            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."
+            )
+            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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py
index 1f890f0..7054c57 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py
new file mode 100644
index 0000000..f471e1a
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py
@@ -0,0 +1,22 @@
+# 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 .hierarchy import BuildHierarchy, BuildHierarchyWithColor
+from .io_obj import LoadObj, LoadObjWithTexture
+from .render import rasterize, interpolate
+'''
+from .io_glb import *
+from .io_obj import *
+from .render import *
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py
new file mode 100644
index 0000000..f1daf7f
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py
@@ -0,0 +1,241 @@
+# 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 base64
+import io
+import os
+
+import numpy as np
+from PIL import Image as PILImage
+from pygltflib import GLTF2
+from scipy.spatial.transform import Rotation as R
+
+
+# Function to extract buffer data
+def get_buffer_data(gltf, buffer_view):
+    buffer = gltf.buffers[buffer_view.buffer]
+    buffer_data = gltf.get_data_from_buffer_uri(buffer.uri)
+    byte_offset = buffer_view.byteOffset if buffer_view.byteOffset else 0
+    byte_length = buffer_view.byteLength
+    return buffer_data[byte_offset:byte_offset + byte_length]
+
+
+# Function to extract attribute data
+def get_attribute_data(gltf, accessor_index):
+    accessor = gltf.accessors[accessor_index]
+    buffer_view = gltf.bufferViews[accessor.bufferView]
+    buffer_data = get_buffer_data(gltf, buffer_view)
+
+    comptype = {5120: np.int8, 5121: np.uint8, 5122: np.int16, 5123: np.uint16, 5125: np.uint32, 5126: np.float32}
+    dtype = comptype[accessor.componentType]
+
+    t2n = {'SCALAR': 1, 'VEC2': 2, 'VEC3': 3, 'VEC4': 4, 'MAT2': 4, 'MAT3': 9, 'MAT4': 16}
+    num_components = t2n[accessor.type]
+
+    # Calculate the correct slice of data
+    byte_offset = accessor.byteOffset if accessor.byteOffset else 0
+    byte_stride = buffer_view.byteStride if buffer_view.byteStride else num_components * np.dtype(dtype).itemsize
+    count = accessor.count
+
+    # Extract the attribute data
+    attribute_data = np.zeros((count, num_components), dtype=dtype)
+    for i in range(count):
+        start = byte_offset + i * byte_stride
+        end = start + num_components * np.dtype(dtype).itemsize
+        attribute_data[i] = np.frombuffer(buffer_data[start:end], dtype=dtype)
+
+    return attribute_data
+
+
+# Function to extract image data
+def get_image_data(gltf, image, folder):
+    if image.uri:
+        if image.uri.startswith('data:'):
+            # Data URI
+            header, encoded = image.uri.split(',', 1)
+            data = base64.b64decode(encoded)
+        else:
+            # External file
+            fn = image.uri
+            if not os.path.isabs(fn):
+                fn = folder + '/' + fn
+            with open(fn, 'rb') as f:
+                data = f.read()
+    else:
+        buffer_view = gltf.bufferViews[image.bufferView]
+        data = get_buffer_data(gltf, buffer_view)
+    return data
+
+
+# Function to convert triangle strip to triangles
+def convert_triangle_strip_to_triangles(indices):
+    triangles = []
+    for i in range(len(indices) - 2):
+        if i % 2 == 0:
+            triangles.append([indices[i], indices[i + 1], indices[i + 2]])
+        else:
+            triangles.append([indices[i], indices[i + 2], indices[i + 1]])
+    return np.array(triangles).reshape(-1, 3)
+
+
+# Function to convert triangle fan to triangles
+def convert_triangle_fan_to_triangles(indices):
+    triangles = []
+    for i in range(1, len(indices) - 1):
+        triangles.append([indices[0], indices[i], indices[i + 1]])
+    return np.array(triangles).reshape(-1, 3)
+
+
+# Function to get the transformation matrix from a node
+def get_node_transform(node):
+    if node.matrix:
+        return np.array(node.matrix).reshape(4, 4).T
+    else:
+        T = np.eye(4)
+        if node.translation:
+            T[:3, 3] = node.translation
+        if node.rotation:
+            R_mat = R.from_quat(node.rotation).as_matrix()
+            T[:3, :3] = R_mat
+        if node.scale:
+            S = np.diag(node.scale + [1])
+            T = T @ S
+        return T
+
+
+def get_world_transform(gltf, node_index, parents, world_transforms):
+    if parents[node_index] == -2:
+        return world_transforms[node_index]
+
+    node = gltf.nodes[node_index]
+    if parents[node_index] == -1:
+        world_transforms[node_index] = get_node_transform(node)
+        parents[node_index] = -2
+        return world_transforms[node_index]
+
+    parent_index = parents[node_index]
+    parent_transform = get_world_transform(gltf, parent_index, parents, world_transforms)
+    world_transforms[node_index] = parent_transform @ get_node_transform(node)
+    parents[node_index] = -2
+    return world_transforms[node_index]
+
+
+def LoadGlb(path):
+    # Load the GLB file using pygltflib
+    gltf = GLTF2().load(path)
+
+    primitives = []
+    images = {}
+    # Iterate through the meshes in the GLB file
+
+    world_transforms = [np.identity(4) for i in range(len(gltf.nodes))]
+    parents = [-1 for i in range(len(gltf.nodes))]
+    for node_index, node in enumerate(gltf.nodes):
+        for idx in node.children:
+            parents[idx] = node_index
+    # for i in range(len(gltf.nodes)):
+    #    get_world_transform(gltf, i, parents, world_transform)
+
+    for node_index, node in enumerate(gltf.nodes):
+        if node.mesh is not None:
+            world_transform = get_world_transform(gltf, node_index, parents, world_transforms)
+            # Iterate through the primitives in the mesh
+            mesh = gltf.meshes[node.mesh]
+            for primitive in mesh.primitives:
+                # Access the attributes of the primitive
+                attributes = primitive.attributes.__dict__
+                mode = primitive.mode if primitive.mode is not None else 4  # Default to TRIANGLES
+                result = {}
+                if primitive.indices is not None:
+                    indices = get_attribute_data(gltf, primitive.indices)
+                    if mode == 4:  # TRIANGLES
+                        face_indices = indices.reshape(-1, 3)
+                    elif mode == 5:  # TRIANGLE_STRIP
+                        face_indices = convert_triangle_strip_to_triangles(indices)
+                    elif mode == 6:  # TRIANGLE_FAN
+                        face_indices = convert_triangle_fan_to_triangles(indices)
+                    else:
+                        continue
+                    result['F'] = face_indices
+
+                # Extract vertex positions
+                if 'POSITION' in attributes and attributes['POSITION'] is not None:
+                    positions = get_attribute_data(gltf, attributes['POSITION'])
+                    # Apply the world transformation to the positions
+                    positions_homogeneous = np.hstack([positions, np.ones((positions.shape[0], 1))])
+                    transformed_positions = (world_transform @ positions_homogeneous.T).T[:, :3]
+                    result['V'] = transformed_positions
+
+                # Extract vertex colors
+                if 'COLOR_0' in attributes and attributes['COLOR_0'] is not None:
+                    colors = get_attribute_data(gltf, attributes['COLOR_0'])
+                    if colors.shape[-1] > 3:
+                        colors = colors[..., :3]
+                    result['VC'] = colors
+
+                # Extract UVs
+                if 'TEXCOORD_0' in attributes and not attributes['TEXCOORD_0'] is None:
+                    uvs = get_attribute_data(gltf, attributes['TEXCOORD_0'])
+                    result['UV'] = uvs
+
+                if primitive.material is not None:
+                    material = gltf.materials[primitive.material]
+                    if (
+                        material.pbrMetallicRoughness is not None 
+                        and material.pbrMetallicRoughness.baseColorTexture is not None
+                    ):
+                        texture_index = material.pbrMetallicRoughness.baseColorTexture.index
+                        texture = gltf.textures[texture_index]
+                        image_index = texture.source
+                        if not image_index in images:
+                            image = gltf.images[image_index]
+                            image_data = get_image_data(gltf, image, os.path.dirname(path))
+                            pil_image = PILImage.open(io.BytesIO(image_data))
+                            if pil_image.mode != 'RGB':
+                                pil_image = pil_image.convert('RGB')
+                            images[image_index] = pil_image
+                        result['TEX'] = image_index
+                    elif material.emissiveTexture is not None:
+                        texture_index = material.emissiveTexture.index
+                        texture = gltf.textures[texture_index]
+                        image_index = texture.source
+                        if not image_index in images:
+                            image = gltf.images[image_index]
+                            image_data = get_image_data(gltf, image, os.path.dirname(path))
+                            pil_image = PILImage.open(io.BytesIO(image_data))
+                            if pil_image.mode != 'RGB':
+                                pil_image = pil_image.convert('RGB')
+                            images[image_index] = pil_image
+                        result['TEX'] = image_index
+                    else:
+                        if material.pbrMetallicRoughness is not None:
+                            base_color = material.pbrMetallicRoughness.baseColorFactor
+                        else:
+                            base_color = np.array([0.8, 0.8, 0.8], dtype=np.float32)
+                        result['MC'] = base_color
+
+                primitives.append(result)
+
+    return primitives, images
+
+
+def RotatePrimitives(primitives, transform):
+    for i in range(len(primitives)):
+        if 'V' in primitives[i]:
+            primitives[i]['V'] = primitives[i]['V'] @ transform.T
+
+
+if __name__ == '__main__':
+    path = 'data/test.glb'
+    LoadGlb(path)
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py
new file mode 100644
index 0000000..e40d500
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py
@@ -0,0 +1,66 @@
+# 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 cv2
+import numpy as np
+
+
+def LoadObj(fn):
+    lines = [l.strip() for l in open(fn)]
+    vertices = []
+    faces = []
+    for l in lines:
+        words = [w for w in l.split(' ') if w != '']
+        if len(words) == 0:
+            continue
+        if words[0] == 'v':
+            v = [float(words[i]) for i in range(1, 4)]
+            vertices.append(v)
+        elif words[0] == 'f':
+            f = [int(words[i]) - 1 for i in range(1, 4)]
+            faces.append(f)
+
+    return np.array(vertices).astype('float32'), np.array(faces).astype('int32')
+
+
+def LoadObjWithTexture(fn, tex_fn):
+    lines = [l.strip() for l in open(fn)]
+    vertices = []
+    vertex_textures = []
+    faces = []
+    face_textures = []
+    for l in lines:
+        words = [w for w in l.split(' ') if w != '']
+        if len(words) == 0:
+            continue
+        if words[0] == 'v':
+            v = [float(words[i]) for i in range(1, len(words))]
+            vertices.append(v)
+        elif words[0] == 'vt':
+            v = [float(words[i]) for i in range(1, len(words))]
+            vertex_textures.append(v)
+        elif words[0] == 'f':
+            f = []
+            ft = []
+            for i in range(1, len(words)):
+                t = words[i].split('/')
+                f.append(int(t[0]) - 1)
+                ft.append(int(t[1]) - 1)
+            for i in range(2, len(f)):
+                faces.append([f[0], f[i - 1], f[i]])
+                face_textures.append([ft[0], ft[i - 1], ft[i]])
+
+    tex_image = cv2.cvtColor(cv2.imread(tex_fn), cv2.COLOR_BGR2RGB)
+    return np.array(vertices).astype('float32'), np.array(vertex_textures).astype('float32'), np.array(faces).astype(
+        'int32'), np.array(face_textures).astype('int32'), tex_image
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py
new file mode 100644
index 0000000..2d4d3f7
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py
@@ -0,0 +1,31 @@
+# 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 custom_rasterizer_kernel
+import torch
+
+
+def rasterize(pos, tri, resolution, clamp_depth=torch.zeros(0), use_depth_prior=0):
+    assert (pos.device == tri.device)
+    findices, barycentric = custom_rasterizer_kernel.rasterize_image(pos[0], tri, clamp_depth, resolution[1],
+                                                                     resolution[0], 1e-6, use_depth_prior)
+    return findices, barycentric
+
+
+def interpolate(col, findices, barycentric, tri):
+    f = findices - 1 + (findices == 0)
+    vcol = col[0, tri.long()[f.long()]]
+    result = barycentric.view(*barycentric.shape, 1) * vcol
+    result = torch.sum(result, axis=-2)
+    return result.view(1, *result.shape)
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py
new file mode 100644
index 0000000..1614ff8
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py
@@ -0,0 +1,13 @@
+# 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.
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp
new file mode 100644
index 0000000..65ab321
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp
@@ -0,0 +1,574 @@
+#include "rasterizer.h"
+#include <fstream>
+
+inline int pos2key(float* p, int resolution) {
+    int x = (p[0] * 0.5 + 0.5) * resolution;
+    int y = (p[1] * 0.5 + 0.5) * resolution;
+    int z = (p[2] * 0.5 + 0.5) * resolution;
+    return (x * resolution + y) * resolution + z;
+}
+
+inline void key2pos(int key, int resolution, float* p) {
+    int x = key / resolution / resolution;
+    int y = key / resolution % resolution;
+    int z = key % resolution;
+    p[0] = ((x + 0.5) / resolution - 0.5) * 2;
+    p[1] = ((y + 0.5) / resolution - 0.5) * 2;
+    p[2] = ((z + 0.5) / resolution - 0.5) * 2;
+}
+
+inline void key2cornerpos(int key, int resolution, float* p) {
+    int x = key / resolution / resolution;
+    int y = key / resolution % resolution;
+    int z = key % resolution;
+    p[0] = ((x + 0.75) / resolution - 0.5) * 2;
+    p[1] = ((y + 0.25) / resolution - 0.5) * 2;
+    p[2] = ((z + 0.75) / resolution - 0.5) * 2;
+}
+
+inline float* pos_ptr(int l, int i, int j, torch::Tensor t) {
+    float* pdata = t.data_ptr<float>();
+    int height = t.size(1);
+    int width = t.size(2);
+    return &pdata[((l * height + i) * width + j) * 4];
+}
+
+struct Grid
+{
+    std::vector<int> seq2oddcorner;
+    std::vector<int> seq2evencorner;
+    std::vector<int> seq2grid;
+    std::vector<int> seq2normal;
+    std::vector<int> seq2neighbor;
+    std::unordered_map<int, int> grid2seq;
+    std::vector<int> downsample_seq;
+    int num_origin_seq;
+    int resolution;
+    int stride;
+};
+
+inline void pos_from_seq(Grid& grid, int seq, float* p) {
+    auto k = grid.seq2grid[seq];
+    key2pos(k, grid.resolution, p);
+}
+
+inline int fetch_seq(Grid& grid, int l, int i, int j, torch::Tensor pdata) {
+    float* p = pos_ptr(l, i, j, pdata);
+    if (p[3] == 0)
+        return -1;
+    auto key = pos2key(p, grid.resolution);
+    int seq = grid.grid2seq[key];
+    return seq;
+}
+
+inline int fetch_last_seq(Grid& grid, int i, int j, torch::Tensor pdata) {
+    int num_layers = pdata.size(0);
+    int l = 0;
+    int idx = fetch_seq(grid, l, i, j, pdata);
+    while (l < num_layers - 1) {
+        l += 1;
+        int new_idx = fetch_seq(grid, l, i, j, pdata);
+        if (new_idx == -1)
+            break;
+        idx = new_idx;
+    }
+    return idx;
+}
+
+inline int fetch_nearest_seq(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) {
+    float p[3];
+    float max_dist = 1e10;
+    int best_idx = -1;
+    int num_layers = pdata.size(0);
+    for (int l = 0; l < num_layers; ++l) {
+        int idx = fetch_seq(grid, l, i, j, pdata);
+        if (idx == -1)
+            break;
+        pos_from_seq(grid, idx, p);
+        float dist = std::abs(d - p[(dim + 2) % 3]);
+        if (dist < max_dist) {
+            max_dist = dist;
+            best_idx = idx;
+        }
+    }
+    return best_idx;
+}
+
+inline int fetch_nearest_seq_layer(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) {
+    float p[3];
+    float max_dist = 1e10;
+    int best_layer = -1;
+    int num_layers = pdata.size(0);
+    for (int l = 0; l < num_layers; ++l) {
+        int idx = fetch_seq(grid, l, i, j, pdata);
+        if (idx == -1)
+            break;
+        pos_from_seq(grid, idx, p);
+        float dist = std::abs(d - p[(dim + 2) % 3]);
+        if (dist < max_dist) {
+            max_dist = dist;
+            best_layer = l;
+        }
+    }
+    return best_layer;
+}
+
+void FetchNeighbor(Grid& grid, int seq, float* pos, int dim, int boundary_info, std::vector<torch::Tensor>& view_layer_positions,
+    int* output_indices)
+{
+    auto t = view_layer_positions[dim];
+    int height = t.size(1);
+    int width = t.size(2);
+    int top = 0;
+    int ci = 0;
+    int cj = 0;
+    if (dim == 0) {
+        ci = (pos[1]/2+0.5)*height;
+        cj = (pos[0]/2+0.5)*width;
+    }
+    else if (dim == 1) {
+        ci = (pos[1]/2+0.5)*height;
+        cj = (pos[2]/2+0.5)*width;
+    }
+    else {
+        ci = (-pos[2]/2+0.5)*height;
+        cj = (pos[0]/2+0.5)*width;
+    }
+    int stride = grid.stride;
+    for (int ni = ci + stride; ni >= ci - stride; ni -= stride) {
+        for (int nj = cj - stride; nj <= cj + stride; nj += stride) {
+            int idx = -1;
+            if (ni == ci && nj == cj)
+                idx = seq;
+            else if (!(ni < 0 || ni >= height || nj < 0 || nj >= width)) {
+                if (boundary_info == -1)
+                    idx = fetch_seq(grid, 0, ni, nj, t);
+                else if (boundary_info == 1)
+                    idx = fetch_last_seq(grid, ni, nj, t);
+                else
+                    idx = fetch_nearest_seq(grid, ni, nj, dim, pos[(dim + 2) % 3], t);
+            }
+            output_indices[top] = idx;
+            top += 1;
+        }
+    }
+}
+
+void DownsampleGrid(Grid& src, Grid& tar)
+{
+    src.downsample_seq.resize(src.seq2grid.size(), -1);
+    tar.resolution = src.resolution / 2;
+    tar.stride = src.stride * 2;
+    float pos[3];
+    std::vector<int> seq2normal_count;
+    for (int i = 0; i < src.seq2grid.size(); ++i) {
+        key2pos(src.seq2grid[i], src.resolution, pos);
+        int k = pos2key(pos, tar.resolution);
+        int s = seq2normal_count.size();
+        if (!tar.grid2seq.count(k)) {
+            tar.grid2seq[k] = tar.seq2grid.size();
+            tar.seq2grid.emplace_back(k);
+            seq2normal_count.emplace_back(0);
+            seq2normal_count.emplace_back(0);
+            seq2normal_count.emplace_back(0);
+            //tar.seq2normal.emplace_back(src.seq2normal[i]);
+        } else {
+            s = tar.grid2seq[k] * 3;
+        }
+        seq2normal_count[s + src.seq2normal[i]] += 1;
+        src.downsample_seq[i] = tar.grid2seq[k];
+    }
+    tar.seq2normal.resize(seq2normal_count.size() / 3);
+    for (int i = 0; i < seq2normal_count.size(); i += 3) {
+        int t = 0;
+        for (int j = 1; j < 3; ++j) {
+            if (seq2normal_count[i + j] > seq2normal_count[i + t])
+                t = j;
+        }
+        tar.seq2normal[i / 3] = t;
+    }
+}
+
+void NeighborGrid(Grid& grid, std::vector<torch::Tensor> view_layer_positions, int v)
+{
+    grid.seq2evencorner.resize(grid.seq2grid.size(), 0);
+    grid.seq2oddcorner.resize(grid.seq2grid.size(), 0);
+    std::unordered_set<int> visited_seq;
+    for (int vd = 0; vd < 3; ++vd) {
+        auto t = view_layer_positions[vd];
+        auto t0 = view_layer_positions[v];
+        int height = t.size(1);
+        int width = t.size(2);
+        int num_layers = t.size(0);
+        int num_view_layers = t0.size(0);
+        for (int i = 0; i < height; ++i) {
+            for (int j = 0; j < width; ++j) {
+                for (int l = 0; l < num_layers; ++l) {
+                    int seq = fetch_seq(grid, l, i, j, t);
+                    if (seq == -1)
+                        break;
+                    int dim = grid.seq2normal[seq];
+                    if (dim != v)
+                        continue;
+
+                    float pos[3];
+                    pos_from_seq(grid, seq, pos);
+
+                    int ci = 0;
+                    int cj = 0;
+                    if (dim == 0) {
+                        ci = (pos[1]/2+0.5)*height;
+                        cj = (pos[0]/2+0.5)*width;
+                    }
+                    else if (dim == 1) {
+                        ci = (pos[1]/2+0.5)*height;
+                        cj = (pos[2]/2+0.5)*width;
+                    }
+                    else {
+                        ci = (-pos[2]/2+0.5)*height;
+                        cj = (pos[0]/2+0.5)*width;
+                    }
+
+                    if ((ci % (grid.stride * 2) < grid.stride) && (cj % (grid.stride * 2) >= grid.stride))
+                        grid.seq2evencorner[seq] = 1;
+
+                    if ((ci % (grid.stride * 2) >= grid.stride) && (cj % (grid.stride * 2) < grid.stride))
+                        grid.seq2oddcorner[seq] = 1;
+
+                    bool is_boundary = false;
+                    if (vd == v) {
+                        if (l == 0 || l == num_layers - 1)
+                            is_boundary = true;
+                        else {
+                            int seq_new = fetch_seq(grid, l + 1, i, j, t);
+                            if (seq_new == -1)
+                                is_boundary = true;
+                        }
+                    }
+                    int boundary_info = 0;
+                    if (is_boundary && (l == 0))
+                        boundary_info = -1;
+                    else if (is_boundary)
+                        boundary_info = 1;
+                    if (visited_seq.count(seq))
+                        continue;
+                    visited_seq.insert(seq);
+
+                    FetchNeighbor(grid, seq, pos, dim, boundary_info, view_layer_positions, &grid.seq2neighbor[seq * 9]);
+                }
+            }
+        }
+    }
+}
+
+void PadGrid(Grid& src, Grid& tar, std::vector<torch::Tensor>& view_layer_positions) {
+    auto& downsample_seq = src.downsample_seq;
+    auto& seq2evencorner = src.seq2evencorner;
+    auto& seq2oddcorner = src.seq2oddcorner;
+    int indices[9];
+    std::vector<int> mapped_even_corners(tar.seq2grid.size(), 0);
+    std::vector<int> mapped_odd_corners(tar.seq2grid.size(), 0);
+    for (int i = 0; i < downsample_seq.size(); ++i) {
+        if (seq2evencorner[i] > 0) {
+            mapped_even_corners[downsample_seq[i]] = 1;
+        }
+        if (seq2oddcorner[i] > 0) {
+            mapped_odd_corners[downsample_seq[i]] = 1;
+        }
+    }
+    auto& tar_seq2normal = tar.seq2normal;
+    auto& tar_seq2grid = tar.seq2grid;
+    for (int i = 0; i < tar_seq2grid.size(); ++i) {
+        if (mapped_even_corners[i] == 1 && mapped_odd_corners[i] == 1)
+            continue;
+        auto k = tar_seq2grid[i];
+        float p[3];
+        key2cornerpos(k, tar.resolution, p);
+
+        int src_key = pos2key(p, src.resolution);
+        if (!src.grid2seq.count(src_key)) {
+            int seq = src.seq2grid.size();
+            src.grid2seq[src_key] = seq;
+            src.seq2evencorner.emplace_back((mapped_even_corners[i] == 0));
+            src.seq2oddcorner.emplace_back((mapped_odd_corners[i] == 0));
+            src.seq2grid.emplace_back(src_key);
+            src.seq2normal.emplace_back(tar_seq2normal[i]);
+            FetchNeighbor(src, seq, p, tar_seq2normal[i], 0, view_layer_positions, indices);
+            for (int j = 0; j < 9; ++j) {
+                src.seq2neighbor.emplace_back(indices[j]);
+            }
+            src.downsample_seq.emplace_back(i);
+        } else {
+            int seq = src.grid2seq[src_key];
+            if (mapped_even_corners[i] == 0)
+                src.seq2evencorner[seq] = 1;
+            if (mapped_odd_corners[i] == 0)
+                src.seq2oddcorner[seq] = 1;
+        }
+    }
+}
+
+std::vector<std::vector<torch::Tensor>> build_hierarchy(std::vector<torch::Tensor> view_layer_positions,
+    std::vector<torch::Tensor> view_layer_normals, int num_level, int resolution)
+{
+    if (view_layer_positions.size() != 3 || num_level < 1) {
+        printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level);
+        return {{},{},{},{}};
+    }
+
+    std::vector<Grid> grids;
+    grids.resize(num_level);
+
+    std::vector<float> seq2pos;
+    auto& seq2grid = grids[0].seq2grid;
+    auto& seq2normal = grids[0].seq2normal;
+    auto& grid2seq = grids[0].grid2seq;
+    grids[0].resolution = resolution;
+    grids[0].stride = 1;
+
+    auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false);
+    auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false);
+
+    for (int v = 0; v < 3; ++v) {
+        int num_layers = view_layer_positions[v].size(0);
+        int height = view_layer_positions[v].size(1);
+        int width = view_layer_positions[v].size(2);
+        float* data = view_layer_positions[v].data_ptr<float>();
+        float* data_normal = view_layer_normals[v].data_ptr<float>();
+        for (int l = 0; l < num_layers; ++l) {
+            for (int i = 0; i < height; ++i) {
+                for (int j = 0; j < width; ++j) {
+                    float* p = &data[(i * width + j) * 4];
+                    float* n = &data_normal[(i * width + j) * 3];
+                    if (p[3] == 0)
+                        continue;
+                    auto k = pos2key(p, resolution);
+                    if (!grid2seq.count(k)) {
+                        int dim = 0;
+                        for (int d = 0; d < 3; ++d) {
+                            if (std::abs(n[d]) > std::abs(n[dim]))
+                                dim = d;
+                        }
+                        dim = (dim + 1) % 3;
+                        grid2seq[k] = seq2grid.size();
+                        seq2grid.emplace_back(k);
+                        seq2pos.push_back(p[0]);
+                        seq2pos.push_back(p[1]);
+                        seq2pos.push_back(p[2]);
+                        seq2normal.emplace_back(dim);
+                    }
+                }
+            }
+            data += (height * width * 4);
+            data_normal += (height * width * 3);
+        }
+    }
+
+    for (int i = 0; i < num_level - 1; ++i) {
+        DownsampleGrid(grids[i], grids[i + 1]);
+    }
+
+    for (int l = 0; l < num_level; ++l) {
+        grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1);
+        grids[l].num_origin_seq = grids[l].seq2grid.size();
+        for (int d = 0; d < 3; ++d) {
+            NeighborGrid(grids[l], view_layer_positions, d);
+        }
+    }
+
+    for (int i = num_level - 2; i >= 0; --i) {
+        PadGrid(grids[i], grids[i + 1], view_layer_positions);
+    }
+    for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) {
+        int k = grids[0].seq2grid[i];
+        float p[3];
+        key2pos(k, grids[0].resolution, p);
+        seq2pos.push_back(p[0]);
+        seq2pos.push_back(p[1]);
+        seq2pos.push_back(p[2]);
+    }
+
+    std::vector<torch::Tensor> texture_positions(2);
+    std::vector<torch::Tensor> grid_neighbors(grids.size());
+    std::vector<torch::Tensor> grid_downsamples(grids.size() - 1);
+    std::vector<torch::Tensor> grid_evencorners(grids.size());
+    std::vector<torch::Tensor> grid_oddcorners(grids.size());
+
+    texture_positions[0] = torch::zeros({static_cast<int64_t>(seq2pos.size() / 3), static_cast<int64_t>(3)}, float_options);
+    texture_positions[1] = torch::zeros({static_cast<int64_t>(seq2pos.size() / 3)}, float_options);
+    float* positions_out_ptr = texture_positions[0].data_ptr<float>();
+    memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size());
+    positions_out_ptr = texture_positions[1].data_ptr<float>();
+    for (int i = 0; i < grids[0].seq2grid.size(); ++i) {
+        positions_out_ptr[i] = (i < grids[0].num_origin_seq);
+    }
+
+    for (int i = 0; i < grids.size(); ++i) {
+        grid_neighbors[i] = torch::zeros({static_cast<int64_t>(grids[i].seq2grid.size()), static_cast<int64_t>(9)}, int64_options);
+        int64_t* nptr = grid_neighbors[i].data_ptr<int64_t>();
+        for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) {
+            nptr[j] = grids[i].seq2neighbor[j];
+        }
+
+        grid_evencorners[i] = torch::zeros({static_cast<int64_t>(grids[i].seq2evencorner.size())}, int64_options);
+        grid_oddcorners[i] = torch::zeros({static_cast<int64_t>(grids[i].seq2oddcorner.size())}, int64_options);
+        int64_t* dptr = grid_evencorners[i].data_ptr<int64_t>();
+        for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) {
+            dptr[j] = grids[i].seq2evencorner[j];
+        }
+        dptr = grid_oddcorners[i].data_ptr<int64_t>();
+        for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) {
+            dptr[j] = grids[i].seq2oddcorner[j];
+        }            
+        if (i + 1 < grids.size()) {
+            grid_downsamples[i] = torch::zeros({static_cast<int64_t>(grids[i].downsample_seq.size())}, int64_options);
+            int64_t* dptr = grid_downsamples[i].data_ptr<int64_t>();
+            for (int j = 0; j < grids[i].downsample_seq.size(); ++j) {
+                dptr[j] = grids[i].downsample_seq[j];
+            }
+        }
+
+    }
+    return {texture_positions, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners};
+}
+
+std::vector<std::vector<torch::Tensor>> build_hierarchy_with_feat(
+    std::vector<torch::Tensor> view_layer_positions,
+    std::vector<torch::Tensor> view_layer_normals,
+    std::vector<torch::Tensor> view_layer_feats,
+    int num_level, int resolution)
+{
+    if (view_layer_positions.size() != 3 || num_level < 1) {
+        printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level);
+        return {{},{},{},{}};
+    }
+
+    std::vector<Grid> grids;
+    grids.resize(num_level);
+
+    std::vector<float> seq2pos;
+    std::vector<float> seq2feat;
+    auto& seq2grid = grids[0].seq2grid;
+    auto& seq2normal = grids[0].seq2normal;
+    auto& grid2seq = grids[0].grid2seq;
+    grids[0].resolution = resolution;
+    grids[0].stride = 1;
+
+    auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false);
+    auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false);
+
+    int feat_channel = 3;
+    for (int v = 0; v < 3; ++v) {
+        int num_layers = view_layer_positions[v].size(0);
+        int height = view_layer_positions[v].size(1);
+        int width = view_layer_positions[v].size(2);
+        float* data = view_layer_positions[v].data_ptr<float>();
+        float* data_normal = view_layer_normals[v].data_ptr<float>();
+        float* data_feat = view_layer_feats[v].data_ptr<float>();
+        feat_channel = view_layer_feats[v].size(3);
+        for (int l = 0; l < num_layers; ++l) {
+            for (int i = 0; i < height; ++i) {
+                for (int j = 0; j < width; ++j) {
+                    float* p = &data[(i * width + j) * 4];
+                    float* n = &data_normal[(i * width + j) * 3];
+                    float* f = &data_feat[(i * width + j) * feat_channel];
+                    if (p[3] == 0)
+                        continue;
+                    auto k = pos2key(p, resolution);
+                    if (!grid2seq.count(k)) {
+                        int dim = 0;
+                        for (int d = 0; d < 3; ++d) {
+                            if (std::abs(n[d]) > std::abs(n[dim]))
+                                dim = d;
+                        }
+                        dim = (dim + 1) % 3;
+                        grid2seq[k] = seq2grid.size();
+                        seq2grid.emplace_back(k);
+                        seq2pos.push_back(p[0]);
+                        seq2pos.push_back(p[1]);
+                        seq2pos.push_back(p[2]);
+                        for (int c = 0; c < feat_channel; ++c) {
+                            seq2feat.emplace_back(f[c]);
+                        }
+                        seq2normal.emplace_back(dim);
+                    }
+                }
+            }
+            data += (height * width * 4);
+            data_normal += (height * width * 3);
+            data_feat += (height * width * feat_channel);
+        }
+    }
+
+    for (int i = 0; i < num_level - 1; ++i) {
+        DownsampleGrid(grids[i], grids[i + 1]);
+    }
+
+    for (int l = 0; l < num_level; ++l) {
+        grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1);
+        grids[l].num_origin_seq = grids[l].seq2grid.size();
+        for (int d = 0; d < 3; ++d) {
+            NeighborGrid(grids[l], view_layer_positions, d);
+        }
+    }
+
+    for (int i = num_level - 2; i >= 0; --i) {
+        PadGrid(grids[i], grids[i + 1], view_layer_positions);
+    }
+    for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) {
+        int k = grids[0].seq2grid[i];
+        float p[3];
+        key2pos(k, grids[0].resolution, p);
+        seq2pos.push_back(p[0]);
+        seq2pos.push_back(p[1]);
+        seq2pos.push_back(p[2]);
+        for (int c = 0; c < feat_channel; ++c) {
+            seq2feat.emplace_back(0.5);
+        }
+    }
+
+    std::vector<torch::Tensor> texture_positions(2);
+    std::vector<torch::Tensor> texture_feats(1);
+    std::vector<torch::Tensor> grid_neighbors(grids.size());
+    std::vector<torch::Tensor> grid_downsamples(grids.size() - 1);
+    std::vector<torch::Tensor> grid_evencorners(grids.size());
+    std::vector<torch::Tensor> grid_oddcorners(grids.size());
+
+    texture_positions[0] = torch::zeros({static_cast<int64_t>(seq2pos.size() / 3), static_cast<int64_t>(3)}, float_options);
+    texture_positions[1] = torch::zeros({static_cast<int64_t>(seq2pos.size() / 3)}, float_options);
+    texture_feats[0] = torch::zeros({static_cast<int64_t>(seq2feat.size() / feat_channel), static_cast<int64_t>(feat_channel)}, float_options);
+    float* positions_out_ptr = texture_positions[0].data_ptr<float>();
+    memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size());
+    positions_out_ptr = texture_positions[1].data_ptr<float>();
+    for (int i = 0; i < grids[0].seq2grid.size(); ++i) {
+        positions_out_ptr[i] = (i < grids[0].num_origin_seq);
+    }
+    float* feats_out_ptr = texture_feats[0].data_ptr<float>();
+    memcpy(feats_out_ptr, seq2feat.data(), sizeof(float) * seq2feat.size());
+
+    for (int i = 0; i < grids.size(); ++i) {
+        grid_neighbors[i] = torch::zeros({static_cast<int64_t>(grids[i].seq2grid.size()), static_cast<int64_t>(9)}, int64_options);
+        int64_t* nptr = grid_neighbors[i].data_ptr<int64_t>();
+        for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) {
+            nptr[j] = grids[i].seq2neighbor[j];
+        }
+        grid_evencorners[i] = torch::zeros({static_cast<int64_t>(grids[i].seq2evencorner.size())}, int64_options);
+        grid_oddcorners[i] = torch::zeros({static_cast<int64_t>(grids[i].seq2oddcorner.size())}, int64_options);
+        int64_t* dptr = grid_evencorners[i].data_ptr<int64_t>();
+        for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) {
+            dptr[j] = grids[i].seq2evencorner[j];
+        }
+        dptr = grid_oddcorners[i].data_ptr<int64_t>();
+        for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) {
+            dptr[j] = grids[i].seq2oddcorner[j];
+        }
+        if (i + 1 < grids.size()) {
+            grid_downsamples[i] = torch::zeros({static_cast<int64_t>(grids[i].downsample_seq.size())}, int64_options);
+            int64_t* dptr = grid_downsamples[i].data_ptr<int64_t>();
+            for (int j = 0; j < grids[i].downsample_seq.size(); ++j) {
+                dptr[j] = grids[i].downsample_seq[j];
+            }
+        }
+    }
+    return {texture_positions, texture_feats, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners};
+}
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp
new file mode 100644
index 0000000..4af6eeb
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp
@@ -0,0 +1,139 @@
+#include "rasterizer.h"
+
+void rasterizeTriangleCPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) {
+    float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0]));
+    float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0]));
+    float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1]));
+    float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1]));
+
+    for (int px = x_min; px < x_max + 1; ++px) {
+        if (px < 0 || px >= width)
+            continue;
+        for (int py = y_min; py < y_max + 1; ++py) {
+            if (py < 0 || py >= height)
+                continue;
+            float vt[2] = {px + 0.5, py + 0.5};
+            float baryCentricCoordinate[3];
+            calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate);
+            if (isBarycentricCoordInBounds(baryCentricCoordinate)) {
+                int pixel = py * width + px;
+                if (zbuffer == 0) {
+                    zbuffer[pixel] = (INT64)(idx + 1);
+                    continue;
+                }
+
+                float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2];
+                float depth_thres = 0;
+                if (d) {
+                    depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation;
+                }
+                
+                int z_quantize = depth * (2<<17);
+                INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1);
+                if (depth < depth_thres)
+                    continue;
+                zbuffer[pixel] = std::min(zbuffer[pixel], token);
+            }
+        }
+    }
+}
+
+void barycentricFromImgcoordCPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces,
+    float* barycentric_map, int pix)
+{
+    INT64 f = zbuffer[pix] % MAXINT;
+    if (f == (MAXINT-1)) {
+        findices[pix] = 0;
+        barycentric_map[pix * 3] = 0;
+        barycentric_map[pix * 3 + 1] = 0;
+        barycentric_map[pix * 3 + 2] = 0;
+        return;
+    }
+    findices[pix] = f;
+    f -= 1;
+    float barycentric[3] = {0, 0, 0};
+    if (f >= 0) {
+        float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f};
+        float* vt0_ptr = V + (F[f * 3] * 4);
+        float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+        float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+        float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f};
+        float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f};
+        float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f};
+
+        calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric);
+
+        barycentric[0] = barycentric[0] / vt0_ptr[3];
+        barycentric[1] = barycentric[1] / vt1_ptr[3];
+        barycentric[2] = barycentric[2] / vt2_ptr[3];
+        float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]);
+        barycentric[0] *= w;
+        barycentric[1] *= w;
+        barycentric[2] *= w;
+
+    }
+    barycentric_map[pix * 3] = barycentric[0];
+    barycentric_map[pix * 3 + 1] = barycentric[1];
+    barycentric_map[pix * 3 + 2] = barycentric[2];
+}
+
+void rasterizeImagecoordsKernelCPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces, int f)
+{
+    float* vt0_ptr = V + (F[f * 3] * 4);
+    float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+    float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+    float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f};
+    float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f};
+    float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f};
+
+    rasterizeTriangleCPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc);
+}
+
+std::vector<torch::Tensor> rasterize_image_cpu(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior)
+{
+    int num_faces = F.size(0);
+    int num_vertices = V.size(0);
+    auto options = torch::TensorOptions().dtype(torch::kInt32).requires_grad(false);
+    auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false);
+    auto findices = torch::zeros({height, width}, options);
+    INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1);
+    auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint;
+
+    if (!use_depth_prior) {
+        for (int i = 0; i < num_faces; ++i) {
+            rasterizeImagecoordsKernelCPU(V.data_ptr<float>(), F.data_ptr<int>(), 0,
+                (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces, i); 
+        }
+    } else {
+        for (int i = 0; i < num_faces; ++i)
+            rasterizeImagecoordsKernelCPU(V.data_ptr<float>(), F.data_ptr<int>(), D.data_ptr<float>(),
+                (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces, i);
+    }
+
+    auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false);
+    auto barycentric = torch::zeros({height, width, 3}, float_options);
+    for (int i = 0; i < width * height; ++i)
+        barycentricFromImgcoordCPU(V.data_ptr<float>(), F.data_ptr<int>(),
+            findices.data_ptr<int>(), (INT64*)z_min.data_ptr<int64_t>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>(), i);
+
+    return {findices, barycentric};
+}
+
+std::vector<torch::Tensor> rasterize_image(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior)
+{
+    int device_id = V.get_device();
+    if (device_id == -1)
+        return rasterize_image_cpu(V, F, D, width, height, occlusion_truncation, use_depth_prior);
+    else
+        return rasterize_image_gpu(V, F, D, width, height, occlusion_truncation, use_depth_prior);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("rasterize_image", &rasterize_image, "Custom image rasterization");
+  m.def("build_hierarchy", &build_hierarchy, "Custom image rasterization");
+  m.def("build_hierarchy_with_feat", &build_hierarchy_with_feat, "Custom image rasterization");
+}
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h
new file mode 100644
index 0000000..cf4f987
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h
@@ -0,0 +1,54 @@
+#ifndef RASTERIZER_H_
+#define RASTERIZER_H_
+
+#include <torch/extension.h>
+#include <vector>
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h> // For CUDA context
+
+#define INT64 unsigned long long
+#define MAXINT 2147483647
+
+__host__ __device__ inline float calculateSignedArea2(float* a, float* b, float* c) {
+    return ((c[0] - a[0]) * (b[1] - a[1]) - (b[0] - a[0]) * (c[1] - a[1]));
+}
+
+__host__ __device__  inline void calculateBarycentricCoordinate(float* a, float* b, float* c, float* p,
+    float* barycentric)
+{
+    float beta_tri = calculateSignedArea2(a, p, c);
+    float gamma_tri = calculateSignedArea2(a, b, p);
+    float area = calculateSignedArea2(a, b, c);
+    if (area == 0) {
+        barycentric[0] = -1.0;
+        barycentric[1] = -1.0;
+        barycentric[2] = -1.0;
+        return;
+    }
+    float tri_inv = 1.0 / area;
+    float beta = beta_tri * tri_inv;
+    float gamma = gamma_tri * tri_inv;
+    float alpha = 1.0 - beta - gamma;
+    barycentric[0] = alpha;
+    barycentric[1] = beta;
+    barycentric[2] = gamma;
+}
+
+__host__ __device__  inline bool isBarycentricCoordInBounds(float* barycentricCoord) {
+    return barycentricCoord[0] >= 0.0 && barycentricCoord[0] <= 1.0 &&
+           barycentricCoord[1] >= 0.0 && barycentricCoord[1] <= 1.0 &&
+           barycentricCoord[2] >= 0.0 && barycentricCoord[2] <= 1.0;
+}
+
+std::vector<torch::Tensor> rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior);
+
+std::vector<std::vector<torch::Tensor>> build_hierarchy(std::vector<torch::Tensor> view_layer_positions, std::vector<torch::Tensor> view_layer_normals, int num_level, int resolution);
+
+std::vector<std::vector<torch::Tensor>> build_hierarchy_with_feat(
+    std::vector<torch::Tensor> view_layer_positions,
+    std::vector<torch::Tensor> view_layer_normals,
+    std::vector<torch::Tensor> view_layer_feats,
+    int num_level, int resolution);
+
+#endif
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu
new file mode 100644
index 0000000..cc6f354
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu
@@ -0,0 +1,127 @@
+#include "rasterizer.h"
+
+__device__ void rasterizeTriangleGPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) {
+    float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0]));
+    float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0]));
+    float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1]));
+    float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1]));
+
+    for (int px = x_min; px < x_max + 1; ++px) {
+        if (px < 0 || px >= width)
+            continue;
+        for (int py = y_min; py < y_max + 1; ++py) {
+            if (py < 0 || py >= height)
+                continue;
+            float vt[2] = {px + 0.5f, py + 0.5f};
+            float baryCentricCoordinate[3];
+            calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate);
+            if (isBarycentricCoordInBounds(baryCentricCoordinate)) {
+                int pixel = py * width + px;
+                if (zbuffer == 0) {
+                    atomicExch(&zbuffer[pixel], (INT64)(idx + 1));
+                    continue;
+                }
+                float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2];
+                float depth_thres = 0;
+                if (d) {
+                    depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation;
+                }
+                
+                int z_quantize = depth * (2<<17);
+                INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1);
+                if (depth < depth_thres)
+                    continue;
+                atomicMin(&zbuffer[pixel], token);
+            }
+        }
+    }
+}
+
+__global__ void barycentricFromImgcoordGPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces,
+    float* barycentric_map)
+{
+    int pix = blockIdx.x * blockDim.x + threadIdx.x;
+    if (pix >= width * height)
+        return;
+    INT64 f = zbuffer[pix] % MAXINT;
+    if (f == (MAXINT-1)) {
+        findices[pix] = 0;
+        barycentric_map[pix * 3] = 0;
+        barycentric_map[pix * 3 + 1] = 0;
+        barycentric_map[pix * 3 + 2] = 0;
+        return;
+    }
+    findices[pix] = f;
+    f -= 1;
+    float barycentric[3] = {0, 0, 0};
+    if (f >= 0) {
+        float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f};
+        float* vt0_ptr = V + (F[f * 3] * 4);
+        float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+        float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+        float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f};
+        float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f};
+        float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f};
+
+        calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric);
+
+        barycentric[0] = barycentric[0] / vt0_ptr[3];
+        barycentric[1] = barycentric[1] / vt1_ptr[3];
+        barycentric[2] = barycentric[2] / vt2_ptr[3];
+        float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]);
+        barycentric[0] *= w;
+        barycentric[1] *= w;
+        barycentric[2] *= w;
+
+    }
+    barycentric_map[pix * 3] = barycentric[0];
+    barycentric_map[pix * 3 + 1] = barycentric[1];
+    barycentric_map[pix * 3 + 2] = barycentric[2];
+}
+
+__global__ void rasterizeImagecoordsKernelGPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces)
+{
+    int f = blockIdx.x * blockDim.x + threadIdx.x;
+    if (f >= num_faces)
+        return; 
+
+    float* vt0_ptr = V + (F[f * 3] * 4);
+    float* vt1_ptr = V + (F[f * 3 + 1] * 4);
+    float* vt2_ptr = V + (F[f * 3 + 2] * 4);
+
+    float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f};
+    float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f};
+    float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f};
+
+    rasterizeTriangleGPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc);
+}
+
+std::vector<torch::Tensor> rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D,
+    int width, int height, float occlusion_truncation, int use_depth_prior)
+{
+    int device_id = V.get_device();
+    cudaSetDevice(device_id);
+    int num_faces = F.size(0);
+    int num_vertices = V.size(0);
+    auto options = torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA, device_id).requires_grad(false);
+    auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA, device_id).requires_grad(false);
+    auto findices = torch::zeros({height, width}, options);
+    INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1);
+    auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint;
+
+    if (!use_depth_prior) {
+        rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(), 0,
+            (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces); 
+    } else {
+        rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr<float>(), F.data_ptr<int>(), D.data_ptr<float>(),
+            (INT64*)z_min.data_ptr<int64_t>(), occlusion_truncation, width, height, num_vertices, num_faces); 
+    }
+
+    auto float_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_id).requires_grad(false);
+    auto barycentric = torch::zeros({height, width, 3}, float_options);
+    barycentricFromImgcoordGPU<<<(width * height + 255)/256, 256>>>(V.data_ptr<float>(), F.data_ptr<int>(),
+        findices.data_ptr<int>(), (INT64*)z_min.data_ptr<int64_t>(), width, height, num_vertices, num_faces, barycentric.data_ptr<float>());
+
+    return {findices, barycentric};
+}
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/setup.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/setup.py
new file mode 100644
index 0000000..3e312a7
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/custom_rasterizer/setup.py
@@ -0,0 +1,26 @@
+from setuptools import setup, find_packages
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
+# build custom rasterizer
+# build with `python setup.py install`
+# nvcc is needed
+
+custom_rasterizer_module = CUDAExtension('custom_rasterizer_kernel', [
+    'lib/custom_rasterizer_kernel/rasterizer.cpp',
+    'lib/custom_rasterizer_kernel/grid_neighbor.cpp',
+    'lib/custom_rasterizer_kernel/rasterizer_gpu.cu',
+])
+
+setup(
+    packages=find_packages(),
+    version='0.1',
+    name='custom_rasterizer',
+    include_package_data=True,
+    package_dir={'': '.'},
+    ext_modules=[
+        custom_rasterizer_module,
+    ],
+    cmdclass={
+        'build_ext': BuildExtension
+    }
+)
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py
index e307c3f..8bb2bf8 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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.
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py
index 289710a..b67727c 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/camera_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.sh b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.sh
deleted file mode 100644
index 056be9d..0000000
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.sh
+++ /dev/null
@@ -1 +0,0 @@
-c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` mesh_processor.cpp -o mesh_processor`python3-config --extension-suffix`
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py
index a96955c..5a731cc 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_processor.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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):
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py
index c85b80e..6f83a36 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_render.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -484,7 +474,7 @@ class MeshRender():
         visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
         normal = normal * visible_mask + \
                  torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 -
-                                                                                    visible_mask)  # Mask out background.
+                                                                                    visible_mask)  
 
         if normalize_rgb:
             normal = (normal + 1) * 0.5
@@ -594,7 +584,7 @@ class MeshRender():
 
         position = position * visible_mask + \
                    torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 -
-                                                                                      visible_mask)  # Mask out background.
+                                                                                      visible_mask) 
         if self.use_antialias:
             position = self.raster_antialias(position, rast_out, pos_clip, self.pos_idx)
 
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py
index ca0ba1a..fa5694a 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/mesh_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py
index 902d842..1bfdb10 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/differentiable_renderer/setup.py
@@ -1,40 +1,55 @@
+# 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
 import platform
 
-# Common compile arguments
-common_compile_args = []
+def get_platform_specific_args():
+    system = platform.system().lower()
+    cpp_std = 'c++14'  # Make configurable if needed
+    
+    if sys.platform == 'win32':
+        compile_args = ['/O2', f'/std:{cpp_std}', '/EHsc', '/MP', '/DWIN32_LEAN_AND_MEAN', '/bigobj']
+        link_args = []
+        extra_includes = []
+    elif system == 'linux':
+        compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', '-pthread']
+        link_args = ['-fPIC', '-pthread']
+        extra_includes = []
+    elif sys.platform == 'darwin':
+        compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra',
+                       '-stdlib=libc++', '-mmacosx-version-min=10.14']
+        link_args = ['-fPIC', '-stdlib=libc++', '-mmacosx-version-min=10.14', '-dynamiclib']
+        extra_includes = []
+    else:
+        raise RuntimeError(f"Unsupported platform: {system}")
+    
+    return compile_args, link_args, extra_includes
 
-if sys.platform == 'win32':
-    # Windows-specific compile arguments
-    extra_compile_args = [
-        '/O2',  # Optimization level
-        '/std:c++14',  # C++ standard (using C++14 for better compatibility)
-        '/EHsc',  # Exception handling model
-        '/MP',  # Multi-process compilation
-        '/DWIN32_LEAN_AND_MEAN',  # Exclude rarely-used Windows headers
-    ]
-    extra_link_args = []
-else:
-    # Linux/Unix compile arguments
-    extra_compile_args = [
-        '-O3',  # Optimization level
-        '-std=c++14',  # C++ standard
-        '-fPIC',  # Position independent code
-    ]
-    extra_link_args = ['-fPIC']
+extra_compile_args, extra_link_args, platform_includes = get_platform_specific_args()
+include_dirs = [pybind11.get_include(), pybind11.get_include(user=True)]
+include_dirs.extend(platform_includes)
 
 ext_modules = [
     Extension(
         "mesh_processor",
         ["mesh_processor.cpp"],
-        include_dirs=[
-            pybind11.get_include(),
-            pybind11.get_include(user=True)
-        ],
+        include_dirs=include_dirs,
         language='c++',
-        extra_compile_args=common_compile_args + extra_compile_args,
+        extra_compile_args=extra_compile_args,
         extra_link_args=extra_link_args,
     ),
 ]
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py
index e307c3f..8bb2bf8 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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.
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py
index 436ce34..38f3777 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/pipeline.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -29,26 +19,164 @@ import numpy as np
 import torch
 import torch.distributed
 import torch.utils.checkpoint
+import transformers
 from PIL import Image
+import diffusers
 from diffusers import (
     AutoencoderKL,
+    DDPMScheduler,
     DiffusionPipeline,
+    EulerAncestralDiscreteScheduler,
+    UNet2DConditionModel,
     ImagePipelineOutput
 )
 from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
 from diffusers.image_processor import PipelineImageInput
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, retrieve_timesteps, \
-    rescale_noise_cfg
-from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, \
+    retrieve_timesteps, rescale_noise_cfg
+from diffusers.schedulers import KarrasDiffusionSchedulers, LCMScheduler
 from diffusers.utils import deprecate
 from einops import rearrange
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
-from .unet.modules import UNet2p5DConditionModel
+from .unet.modules import UNet2p5DConditionModel, \
+    compute_multi_resolution_mask, compute_multi_resolution_discrete_voxel_indice
+
+def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
+    """
+    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+    Args:
+        timesteps (`torch.Tensor`):
+            generate embedding vectors at these timesteps
+        embedding_dim (`int`, *optional*, defaults to 512):
+            dimension of the embeddings to generate
+        dtype:
+            data type of the generated embeddings
+
+    Returns:
+        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+    """
+    assert len(w.shape) == 1
+    w = w * 1000.0
+
+    half_dim = embedding_dim // 2
+    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+    emb = w.to(dtype)[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1))
+    assert emb.shape == (w.shape[0], embedding_dim)
+    return emb
 
 
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+    return x[(...,) + (None,) * dims_to_append]
+
+
+# From LCMScheduler.get_scalings_for_boundary_condition_discrete
+def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
+    scaled_timestep = timestep_scaling * timestep
+    c_skip = sigma_data ** 2 / (scaled_timestep ** 2 + sigma_data ** 2)
+    c_out = scaled_timestep / (scaled_timestep ** 2 + sigma_data ** 2) ** 0.5
+    return c_skip, c_out
+
+
+# Compare LCMScheduler.step, Step 4
+def get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen):
+    alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen)
+    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen)
+    model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen)
+    if prediction_type == "epsilon":
+        pred_x_0 = (sample - sigmas * model_output) / alphas
+    elif prediction_type == "sample":
+        pred_x_0 = model_output
+    elif prediction_type == "v_prediction":
+        pred_x_0 = alphas * sample - sigmas * model_output
+    else:
+        raise ValueError(
+            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
+            f" are supported."
+        )
+
+    return pred_x_0
+
+
+# Based on step 4 in DDIMScheduler.step
+def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen):
+    alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen)
+    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen)
+    model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen)
+    if prediction_type == "epsilon":
+        pred_epsilon = model_output
+    elif prediction_type == "sample":
+        pred_epsilon = (sample - alphas * model_output) / sigmas
+    elif prediction_type == "v_prediction":
+        pred_epsilon = alphas * model_output + sigmas * sample
+    else:
+        raise ValueError(
+            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
+            f" are supported."
+        )
+
+    return pred_epsilon
+    
+def extract_into_tensor(a, t, x_shape, N_gen):
+    # b, *_ = t.shape
+    out = a.gather(-1, t)
+    out = out.repeat(N_gen)
+    out = rearrange(out, '(b n) -> b n', n=N_gen)
+    b, c, *_ = out.shape
+    return out.reshape(b, c, *((1,) * (len(x_shape) - 2)))
+
+class DDIMSolver:
+    def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
+        # DDIM sampling parameters
+        step_ratio = timesteps // ddim_timesteps
+        self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
+        self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
+        self.ddim_alpha_cumprods_prev = np.asarray(
+            [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
+        )
+        # convert to torch tensors
+        self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
+        self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
+        self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
+
+    def to(self, device):
+        self.ddim_timesteps = self.ddim_timesteps.to(device)
+        self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
+        self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
+        return self
+
+    def ddim_step(self, pred_x0, pred_noise, timestep_index, N_gen):
+        alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape, N_gen)
+        dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
+        x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev
+
+
+@torch.no_grad()
+def update_ema(target_params, source_params, rate=0.99):
+    """
+    Update target parameters to be closer to those of source parameters using
+    an exponential moving average.
+
+    :param target_params: the target parameter sequence.
+    :param source_params: the source parameter sequence.
+    :param rate: the EMA rate (closer to 1 means slower).
+    """
+
+    for targ, src in zip(target_params, source_params):
+        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
+        
 def to_rgb_image(maybe_rgba: Image.Image):
     if maybe_rgba.mode == 'RGB':
         return maybe_rgba
@@ -87,9 +215,18 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
             safety_checker=safety_checker,
             feature_extractor=torch.compile(feature_extractor) if use_torch_compile else feature_extractor,
         )
+        self.solver = DDIMSolver(
+            scheduler.alphas_cumprod.numpy(),
+            timesteps=scheduler.config.num_train_timesteps,
+            ddim_timesteps=30,
+        ).to('cuda')
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.is_turbo = False
 
+    def set_turbo(self, is_turbo: bool):
+        self.is_turbo = is_turbo
+        
     @torch.no_grad()
     def encode_images(self, images):
         B = images.shape[0]
@@ -119,17 +256,23 @@ 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)
 
-        image = to_rgb_image(image)
+        if not isinstance(image, List):
+            image = [image]
+            
+        image = [to_rgb_image(img) for img in image]
 
-        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 = [torch.tensor(np.array(img) / 255.0) for img in image]
+        image_vae = [img_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0) for img_vae in image_vae]
+        image_vae = torch.cat(image_vae, dim=1)
+        image_vae = image_vae.to(device=device, dtype=self.vae.dtype)
 
-        batch_size = image_vae.shape[0]
+        batch_size, N_ref = image_vae.shape[0], image_vae.shape[1]
         assert batch_size == 1
         assert num_images_per_prompt == 1
 
@@ -165,24 +308,34 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
             if isinstance(cached_condition["position_imgs"], List):
                 cached_condition["position_imgs"] = convert_pil_list_to_tensor(cached_condition["position_imgs"])
 
+            cached_condition['position_maps'] = cached_condition['position_imgs']            
             cached_condition["position_imgs"] = self.encode_images(cached_condition["position_imgs"])
 
         if 'camera_info_gen' in cached_condition:
             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
 
-        if guidance_scale > 1:
+        if self.is_turbo:
+            if 'position_maps' in cached_condition:
+                cached_condition['position_attn_mask'] = (
+                    compute_multi_resolution_mask(cached_condition['position_maps'])
+                )
+                cached_condition['position_voxel_indices'] = (
+                    compute_multi_resolution_discrete_voxel_indice(cached_condition['position_maps'])
+                )
+            
+        if (guidance_scale > 1) and (not self.is_turbo):
             negative_ref_latents = torch.zeros_like(cached_condition['ref_latents'])
             cached_condition['ref_latents'] = torch.cat([negative_ref_latents, cached_condition['ref_latents']])
             cached_condition['ref_scale'] = torch.as_tensor([0.0, 1.0]).to(cached_condition['ref_latents'])
@@ -321,7 +474,8 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
                 plain tuple.
             cross_attention_kwargs (`dict`, *optional*):
                 A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+                [`self.processor`]
+                (https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
             guidance_rescale (`float`, *optional*, defaults to 0.0):
                 Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
@@ -356,13 +510,15 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
             deprecate(
                 "callback",
                 "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+                "Passing `callback` as an input argument to `__call__` is deprecated,",
+                "consider using `callback_on_step_end`",
             )
         if callback_steps is not None:
             deprecate(
                 "callback_steps",
                 "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated,",
+                "consider using `callback_on_step_end`",
             )
 
         if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
@@ -412,7 +568,7 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
             prompt,
             device,
             num_images_per_prompt,
-            self.do_classifier_free_guidance,
+            self.do_classifier_free_guidance if self.is_turbo else False,
             negative_prompt,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
@@ -423,7 +579,7 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
-        if self.do_classifier_free_guidance:
+        if (self.do_classifier_free_guidance) and (not self.is_turbo):
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
         if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
@@ -432,13 +588,21 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
                 ip_adapter_image_embeds,
                 device,
                 batch_size * num_images_per_prompt,
-                self.do_classifier_free_guidance,
+                self.do_classifier_free_guidance if self.is_turbo else False,
             )
 
-        # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, timesteps, sigmas
-        )
+        # 4. Prepare 
+        if self.is_turbo:
+            bsz = 3
+            N_gen = 15
+            index = torch.range(29, 0, -bsz, device='cuda').long()
+            timesteps = self.solver.ddim_timesteps[index]
+            self.scheduler.set_timesteps(timesteps=timesteps.cpu(), device='cuda')
+        else:
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler, num_inference_steps, device, timesteps, sigmas
+            )
+            
         assert num_images_per_prompt == 1
         # 5. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
@@ -481,7 +645,11 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
 
                 # expand the latents if we are doing classifier free guidance
                 latents = rearrange(latents, '(b n) c h w -> b n c h w', n=kwargs['num_in_batch'])
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = (
+                    torch.cat([latents] * 2) 
+                    if ((self.do_classifier_free_guidance) and (not self.is_turbo)) 
+                    else latents
+                )
                 latent_model_input = rearrange(latent_model_input, 'b n c h w -> (b n) c h w')
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                 latent_model_input = rearrange(latent_model_input, '(b n) c h w ->b n c h w', n=kwargs['num_in_batch'])
@@ -499,11 +667,11 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
                 )[0]
                 latents = rearrange(latents, 'b n c h w -> (b n) c h w')
                 # perform guidance
-                if self.do_classifier_free_guidance:
+                if (self.do_classifier_free_guidance) and (not self.is_turbo):
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
-                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                if (self.do_classifier_free_guidance) and (self.guidance_rescale > 0.0) and (not self.is_turbo):
                     # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                     noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
 
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py
index e307c3f..8bb2bf8 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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.
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py
index 5d16bc6..f558cd7 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/hunyuanpaint/unet/modules.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -22,14 +12,14 @@
 # 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
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from diffusers.models import UNet2DConditionModel
 from diffusers.models.attention_processor import Attention
 from diffusers.models.transformers.transformer_2d import BasicTransformerBlock
@@ -40,7 +30,9 @@ def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim:
     # "feed_forward_chunk_size" can be used to save memory
     if hidden_states.shape[chunk_dim] % chunk_size != 0:
         raise ValueError(
-            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}"
+            f"has to be divisible by chunk size: {chunk_size}."
+            f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
         )
 
     num_chunks = hidden_states.shape[chunk_dim] // chunk_size
@@ -52,12 +44,13 @@ def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim:
 
 
 class Basic2p5DTransformerBlock(torch.nn.Module):
-    def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True) -> None:
+    def __init__(self, transformer: BasicTransformerBlock,layer_name,use_ma=True,use_ra=True,is_turbo=False) -> None:
         super().__init__()
         self.transformer = transformer
         self.layer_name = layer_name
         self.use_ma = use_ma
         self.use_ra = use_ra
+        self.is_turbo = is_turbo
 
         # multiview attn
         if self.use_ma:
@@ -84,6 +77,23 @@ class Basic2p5DTransformerBlock(torch.nn.Module):
                 upcast_attention=self.attn1.upcast_attention,
                 out_bias=True,
             )
+        if self.is_turbo:
+            self._initialize_attn_weights()
+
+    def _initialize_attn_weights(self):
+
+        if self.use_ma:
+            self.attn_multiview.load_state_dict(self.attn1.state_dict()) 
+            with torch.no_grad():
+                for layer in self.attn_multiview.to_out:
+                    for param in layer.parameters():
+                        param.zero_()
+        if self.use_ra:
+            self.attn_refview.load_state_dict(self.attn1.state_dict()) 
+            with torch.no_grad():
+                for layer in self.attn_refview.to_out:
+                    for param in layer.parameters():
+                        param.zero_()
 
     def __getattr__(self, name: str):
         try:
@@ -110,8 +120,15 @@ class Basic2p5DTransformerBlock(torch.nn.Module):
         cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
         num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1)
         mode = cross_attention_kwargs.pop('mode', None)
-        mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
-        ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
+        if not self.is_turbo:
+            mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
+            ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
+        else:
+            position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None)
+            position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None)
+            mva_scale = 1.0
+            ref_scale = 1.0
+            
         condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)
 
         if self.norm_type == "ada_norm":
@@ -158,8 +175,10 @@ class Basic2p5DTransformerBlock(torch.nn.Module):
 
         # 1.2 Reference Attention
         if 'w' in mode:
-            condition_embed_dict[self.layer_name] = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c',
-                                                              n=num_in_batch)  # B, (N L), C
+            condition_embed_dict[self.layer_name] = rearrange(
+                norm_hidden_states, '(b n) l c -> b (n l) c',
+                n=num_in_batch
+            )  # B, (N L), C
 
         if 'r' in mode and self.use_ra:
             condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1,
@@ -172,12 +191,15 @@ class Basic2p5DTransformerBlock(torch.nn.Module):
                 attention_mask=None,
                 **cross_attention_kwargs
             )
-            ref_scale_timing = ref_scale
-            if isinstance(ref_scale, torch.Tensor):
-                ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
-                for _ in range(attn_output.ndim - 1):
-                    ref_scale_timing = ref_scale_timing.unsqueeze(-1)
+            if not self.is_turbo:
+                ref_scale_timing = ref_scale
+                if isinstance(ref_scale, torch.Tensor):
+                    ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
+                    for _ in range(attn_output.ndim - 1):
+                        ref_scale_timing = ref_scale_timing.unsqueeze(-1)
+                        
             hidden_states = ref_scale_timing * attn_output + hidden_states
+
             if hidden_states.ndim == 4:
                 hidden_states = hidden_states.squeeze(1)
 
@@ -185,14 +207,31 @@ class Basic2p5DTransformerBlock(torch.nn.Module):
         if num_in_batch > 1 and self.use_ma:
             multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch)
 
-            attn_output = self.attn_multiview(
-                multivew_hidden_states,
-                encoder_hidden_states=multivew_hidden_states,
-                **cross_attention_kwargs
-            )
+            if self.is_turbo:
+                position_mask = None
+                if position_attn_mask is not None:
+                    if multivew_hidden_states.shape[1] in position_attn_mask:
+                        position_mask = position_attn_mask[multivew_hidden_states.shape[1]]
+                position_indices = None
+                if position_voxel_indices is not None:
+                    if multivew_hidden_states.shape[1] in position_voxel_indices:
+                        position_indices = position_voxel_indices[multivew_hidden_states.shape[1]]
+                attn_output = self.attn_multiview(
+                    multivew_hidden_states,
+                    encoder_hidden_states=multivew_hidden_states,
+                    attention_mask=position_mask,
+                    position_indices=position_indices,
+                    **cross_attention_kwargs
+                )
+            else:
+                attn_output = self.attn_multiview(
+                    multivew_hidden_states,
+                    encoder_hidden_states=multivew_hidden_states,
+                    **cross_attention_kwargs
+                )
 
             attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch)
-
+            
             hidden_states = mva_scale * attn_output + hidden_states
             if hidden_states.ndim == 4:
                 hidden_states = hidden_states.squeeze(1)
@@ -259,6 +298,108 @@ class Basic2p5DTransformerBlock(torch.nn.Module):
 
         return hidden_states
 
+@torch.no_grad()
+def compute_voxel_grid_mask(position, grid_resolution=8):
+
+    position = position.half()    
+    B,N,_,H,W = position.shape
+    assert H%grid_resolution==0 and W%grid_resolution==0
+
+    valid_mask = (position != 1).all(dim=2, keepdim=True)
+    valid_mask = valid_mask.expand_as(position)
+    position[valid_mask==False] = 0
+
+    
+    position = rearrange(
+        position,
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+    valid_mask = rearrange(
+        valid_mask, 
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+
+    grid_position = position.sum(dim=(-2, -1))
+    count_masked = valid_mask.sum(dim=(-2, -1))
+
+    grid_position = grid_position / count_masked.clamp(min=1)
+    grid_position[count_masked<5] = 0
+
+    grid_position = grid_position.permute(0,1,4,2,3)
+    grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c')
+
+    grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4)  # 形状变为 B, N, 1, L, 1, 3
+    grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3)  # 形状变为 B, 1, N, 1, L, 3
+
+    # 计算欧氏距离
+    distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1)  # 形状为 B, N, N, L, L
+
+    weights = distances
+    grid_distance = 1.73/grid_resolution
+    
+    #weights = weights*-32
+    #weights = weights.clamp(min=-10000.0)
+    
+    weights = weights< grid_distance
+
+    return weights
+    
+def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]):
+    position_attn_mask = {}
+    with torch.no_grad():
+        for grid_resolution in grid_resolutions:
+            position_mask = compute_voxel_grid_mask(position_maps, grid_resolution)
+            position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)')
+            position_attn_mask[position_mask.shape[1]] = position_mask
+    return position_attn_mask
+
+@torch.no_grad()
+def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128):
+
+    position = position.half()    
+    B,N,_,H,W = position.shape
+    assert H%grid_resolution==0 and W%grid_resolution==0
+
+    valid_mask = (position != 1).all(dim=2, keepdim=True)
+    valid_mask = valid_mask.expand_as(position)
+    position[valid_mask==False] = 0
+    
+    position = rearrange(
+        position, 
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+    valid_mask = rearrange(
+        valid_mask, 
+        'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', 
+        num_h=grid_resolution, num_w=grid_resolution
+    )
+
+    grid_position = position.sum(dim=(-2, -1))
+    count_masked = valid_mask.sum(dim=(-2, -1))
+
+    grid_position = grid_position / count_masked.clamp(min=1)
+    grid_position[count_masked<5] = 0
+
+    grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W
+    voxel_indices = grid_position * (voxel_resolution - 1)
+    voxel_indices = torch.round(voxel_indices).long()
+    return voxel_indices
+    
+def compute_multi_resolution_discrete_voxel_indice(
+    position_maps, 
+    grid_resolutions=[64, 32, 16, 8], 
+    voxel_resolutions=[512, 256, 128, 64]
+):
+    voxel_indices = {}
+    with torch.no_grad():
+        for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions):
+            voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution)
+            voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c')
+            voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution}
+    return voxel_indices
 
 class UNet2p5DConditionModel(torch.nn.Module):
     def __init__(self, unet: UNet2DConditionModel) -> None:
@@ -269,11 +410,12 @@ class UNet2p5DConditionModel(torch.nn.Module):
         self.use_ra = True
         self.use_camera_embedding = True
         self.use_dual_stream = True
+        self.is_turbo = False
 
         if self.use_dual_stream:
             self.unet_dual = copy.deepcopy(unet)
             self.init_attention(self.unet_dual)
-        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra)
+        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo)
         self.init_condition()
         self.init_camera_embedding()
 
@@ -313,33 +455,39 @@ class UNet2p5DConditionModel(torch.nn.Module):
             self.max_num_gen_image = 12 * 3 + 4 * 2
             self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim)
 
-    def init_attention(self, unet, use_ma=False, use_ra=False):
+    def init_attention(self, unet, use_ma=False, use_ra=False, is_turbo=False):
 
         for down_block_i, down_block in enumerate(unet.down_blocks):
             if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention:
                 for attn_i, attn in enumerate(down_block.attentions):
                     for transformer_i, transformer in enumerate(attn.transformer_blocks):
                         if isinstance(transformer, BasicTransformerBlock):
-                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer,
-                                                                                               f'down_{down_block_i}_{attn_i}_{transformer_i}',
-                                                                                               use_ma, use_ra)
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(
+                                transformer,
+                                f'down_{down_block_i}_{attn_i}_{transformer_i}',
+                                use_ma, use_ra, is_turbo
+                            )
 
         if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention:
             for attn_i, attn in enumerate(unet.mid_block.attentions):
                 for transformer_i, transformer in enumerate(attn.transformer_blocks):
                     if isinstance(transformer, BasicTransformerBlock):
-                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer,
-                                                                                           f'mid_{attn_i}_{transformer_i}',
-                                                                                           use_ma, use_ra)
+                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(
+                            transformer,
+                            f'mid_{attn_i}_{transformer_i}',
+                            use_ma, use_ra, is_turbo
+                        )
 
         for up_block_i, up_block in enumerate(unet.up_blocks):
             if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention:
                 for attn_i, attn in enumerate(up_block.attentions):
                     for transformer_i, transformer in enumerate(attn.transformer_blocks):
                         if isinstance(transformer, BasicTransformerBlock):
-                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer,
-                                                                                               f'up_{up_block_i}_{attn_i}_{transformer_i}',
-                                                                                               use_ma, use_ra)
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(
+                                transformer,
+                                f'up_{up_block_i}_{attn_i}_{transformer_i}',
+                                use_ma, use_ra, is_turbo
+                            )
 
     def __getattr__(self, name: str):
         try:
@@ -416,6 +564,22 @@ class UNet2p5DConditionModel(torch.nn.Module):
         mva_scale = cached_condition.get('mva_scale', 1.0)
         ref_scale = cached_condition.get('ref_scale', 1.0)
 
+        if self.is_turbo:
+            cross_attention_kwargs_ = {
+                'mode': 'r', 'num_in_batch': N_gen,
+                'condition_embed_dict': condition_embed_dict,
+                'position_attn_mask':position_attn_mask, 
+                'position_voxel_indices':position_voxel_indices,
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            }
+        else:
+            cross_attention_kwargs_ = {
+                'mode': 'r', 'num_in_batch': N_gen,
+                'condition_embed_dict': condition_embed_dict,
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            }
         return self.unet(
             sample, timestep,
             encoder_hidden_states_gen, *args,
@@ -431,10 +595,5 @@ class UNet2p5DConditionModel(torch.nn.Module):
                 if mid_block_res_sample is not None else None
             ),
             return_dict=False,
-            cross_attention_kwargs={
-                'mode': 'r', 'num_in_batch': N_gen,
-                'condition_embed_dict': condition_embed_dict,
-                'mva_scale': mva_scale,
-                'ref_scale': ref_scale,
-            },
+            cross_attention_kwargs=cross_attention_kwargs_,
         )
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py
index 00bd8da..33cbebc 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/pipelines.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -24,17 +14,17 @@
 
 
 import logging
-import os
-
 import numpy as np
+import os
 import torch
 from PIL import Image
+from typing import List, Union, Optional
 
-import comfy.utils
 
 from .differentiable_renderer.mesh_render import MeshRender
 from .utils.dehighlight_utils import Light_Shadow_Remover
 from .utils.multiview_utils import Multiview_Diffusion_Net
+from .utils.imagesuper_utils import Image_Super_Net
 from .utils.uv_warp_utils import mesh_uv_wrap
 
 logger = logging.getLogger(__name__)
@@ -42,7 +32,7 @@ logger = logging.getLogger(__name__)
 
 class Hunyuan3DTexGenConfig:
 
-    def __init__(self, light_remover_ckpt_path, multiview_ckpt_path):
+    def __init__(self, light_remover_ckpt_path, multiview_ckpt_path, subfolder_name):
         self.device = 'cuda'
         self.light_remover_ckpt_path = light_remover_ckpt_path
         self.multiview_ckpt_path = multiview_ckpt_path
@@ -52,24 +42,30 @@ class Hunyuan3DTexGenConfig:
         self.candidate_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05]
 
         self.render_size = 2048
-        self.texture_size = 1024
+        self.texture_size = 2048
         self.bake_exp = 4
         self.merge_method = 'fast'
 
+        self.pipe_dict = {'hunyuan3d-paint-v2-0': 'hunyuanpaint', 'hunyuan3d-paint-v2-0-turbo': 'hunyuanpaint-turbo'}
+        self.pipe_name = self.pipe_dict[subfolder_name]
+
 
 class Hunyuan3DPaintPipeline:
     @classmethod
-    def from_pretrained(cls, model_path, device='cuda', dtype=torch.float16, **kwargs):
-        # try local path	
-        delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0')
-        multiview_model_path = os.path.join(model_path, 'hunyuan3d-paint-v2-0')
-        
-        if not (os.path.exists(delight_model_path) and os.path.exists(multiview_model_path)):
-            raise FileNotFoundError(f"Model path {delight_model_path} or {multiview_model_path} not found")
-
-        return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path))
+    def from_pretrained(cls, model_path, subfolder='hunyuan3d-paint-v2-0-turbo'):
+        delight_model_path   = os.path.join(model_path, 'hunyuan3d-delight-v2-0')
+        multiview_model_path = os.path.join(model_path, subfolder)
 
+        if os.path.exists(delight_model_path) and os.path.exists(multiview_model_path):
+            return cls(Hunyuan3DTexGenConfig(
+                delight_model_path,
+                multiview_model_path,
+                subfolder
+            ))
 
+        raise FileNotFoundError(
+            f"Required models not found in local paths: {delight_model_path} or {multiview_model_path}"
+        )
     def __init__(self, config):
         self.config = config
         self.models = {}
@@ -79,22 +75,27 @@ class Hunyuan3DPaintPipeline:
 
         self.load_models()
 
+    def to(self, device=None, dtype=None):   
+        if device is not None:
+            for m in self.models.values():
+                m.pipeline.to(device)
+            self.render.to(device)
+        if dtype is not None:
+            for m in self.models.values():
+                m.pipeline.to(dtype=dtype)
+        return self
+
     def load_models(self):
         # empty cude cache
         torch.cuda.empty_cache()
         # Load model
         self.models['delight_model'] = Light_Shadow_Remover(self.config)
         self.models['multiview_model'] = Multiview_Diffusion_Net(self.config)
-        
-    def to(self, device=None, dtype=None):
-        if device is not None:
-            for model in self.models.values():
-                model.pipeline.to(device)
-        if dtype is not None:
-            for model in self.models.values():
-                model.pipeline.to(dtype=dtype)
+        # self.models['super_model'] = Image_Super_Net(self.config)
 
-        return self
+    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 = []
@@ -140,7 +141,7 @@ 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
@@ -178,26 +179,25 @@ class Hunyuan3DPaintPipeline:
     @torch.no_grad()
     def __call__(self, mesh, image):
 
-        comfy_pbar = comfy.utils.ProgressBar(4)
-        step_num_i = 0
+        if not isinstance(image, List):
+            image = [image]
 
-        print("Load image and predict delight")
-        if isinstance(image, str):
-            image_prompt = Image.open(image)
-        else:
-            image_prompt = image
+        images_prompt = []
+        for i in range(len(image)):
+            if isinstance(image[i], str):
+                image_prompt = Image.open(image[i])
+            else:
+                image_prompt = image[i]
+            images_prompt.append(image_prompt)
+            
+        images_prompt = [self.recenter_image(image_prompt) for image_prompt in images_prompt]
 
-        image_prompt = self.recenter_image(image_prompt)
-        image_prompt = self.models['delight_model'](image_prompt)
-
-        step_num_i += 1
-        comfy_pbar.update_absolute(step_num_i)
+        images_prompt = [self.models['delight_model'](image_prompt) for image_prompt in images_prompt]
 
         mesh = mesh_uv_wrap(mesh)
 
-        print("Rendering normal and position maps of the mesh")
         self.render.load_mesh(mesh)
-        
+
         selected_camera_elevs, selected_camera_azims, selected_view_weights = \
             self.config.candidate_camera_elevs, self.config.candidate_camera_azims, self.config.candidate_view_weights
 
@@ -205,32 +205,21 @@ class Hunyuan3DPaintPipeline:
             selected_camera_elevs, selected_camera_azims, use_abs_coor=True)
         position_maps = self.render_position_multiview(
             selected_camera_elevs, selected_camera_azims)
-        
-        step_num_i += 1
-        comfy_pbar.update_absolute(step_num_i)
 
-        print("Predicting multiview texture")
         camera_info = [(((azim // 30) + 9) % 12) // {-20: 1, 0: 1, 20: 1, -90: 3, 90: 3}[
             elev] + {-20: 0, 0: 12, 20: 24, -90: 36, 90: 40}[elev] for azim, elev in
                        zip(selected_camera_azims, selected_camera_elevs)]
-        multiviews = self.models['multiview_model'](image_prompt, normal_maps + position_maps, camera_info)
+        multiviews = self.models['multiview_model'](images_prompt, normal_maps + position_maps, camera_info)
 
         for i in range(len(multiviews)):
+            # multiviews[i] = self.models['super_model'](multiviews[i])
             multiviews[i] = multiviews[i].resize(
                 (self.config.render_size, self.config.render_size))
-        
-        step_num_i += 1
-        comfy_pbar.update_absolute(step_num_i)
 
-        print("Baking texture on to the mesh")
         texture, mask = self.bake_from_multiview(multiviews,
                                                  selected_camera_elevs, selected_camera_azims, selected_view_weights,
                                                  method=self.config.merge_method)
 
-        step_num_i += 1
-        comfy_pbar.update_absolute(step_num_i)
-
-        print("Inpainting texture")        
         mask_np = (mask.squeeze(-1).cpu().numpy() * 255).astype(np.uint8)
 
         texture = self.texture_inpaint(texture, mask_np)
@@ -238,7 +227,4 @@ class Hunyuan3DPaintPipeline:
         self.render.set_texture(texture)
         textured_mesh = self.render.save_mesh()
 
-        step_num_i += 1
-        comfy_pbar.update_absolute(step_num_i)
-
         return textured_mesh
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py
index e307c3f..8bb2bf8 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/__init__.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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.
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py
index 0a09c17..34df204 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/alignImg4Tex_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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, \
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py
index e0374fc..383a515 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/counter_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py
index d9724ae..9b52368 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/dehighlight_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -44,6 +34,36 @@ class Light_Shadow_Remover():
         pipeline.set_progress_bar_config(disable=True)
 
         self.pipeline = pipeline.to(self.device, torch.float16)
+    
+    def recorrect_rgb(self, src_image, target_image, alpha_channel, scale=0.95):
+        
+        def flat_and_mask(bgr, a):
+            mask = torch.where(a > 0.5, True, False)
+            bgr_flat = bgr.reshape(-1, bgr.shape[-1])
+            mask_flat = mask.reshape(-1)
+            bgr_flat_masked = bgr_flat[mask_flat, :]
+            return bgr_flat_masked
+        
+        src_flat = flat_and_mask(src_image, alpha_channel)
+        target_flat = flat_and_mask(target_image, alpha_channel)
+        corrected_bgr = torch.zeros_like(src_image)
+
+        for i in range(3): 
+            src_mean, src_stddev = torch.mean(src_flat[:, i]), torch.std(src_flat[:, i])
+            target_mean, target_stddev = torch.mean(target_flat[:, i]), torch.std(target_flat[:, i])
+            corrected_bgr[:, :, i] = torch.clamp(
+                (src_image[:, :, i] - scale * src_mean) * 
+                (target_stddev / src_stddev) + scale * target_mean, 
+                0, 1)
+
+        src_mse = torch.mean((src_image - target_image) ** 2)
+        modify_mse = torch.mean((corrected_bgr - target_image) ** 2)
+        if src_mse < modify_mse:
+            corrected_bgr = torch.cat([src_image, alpha_channel], dim=-1)
+        else: 
+            corrected_bgr = torch.cat([corrected_bgr, alpha_channel], dim=-1)
+
+        return corrected_bgr
 
     @torch.no_grad()
     def __call__(self, image):
@@ -81,4 +101,10 @@ class Light_Shadow_Remover():
             guidance_scale=self.cfg_text,
         ).images[0]
 
+        image_tensor = torch.tensor(np.array(image)/255.0).to(self.device)
+        rgb_src = image_tensor[:,:,:3]
+        image = self.recorrect_rgb(rgb_src, rgb_target, alpha)
+        image = image[:,:,:3]*image[:,:,3:] + torch.ones_like(image[:,:,:3])*(1.0-image[:,:,3:])
+        image = Image.fromarray((image.cpu().numpy()*255).astype(np.uint8))
+
         return image
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/imagesuper_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/imagesuper_utils.py
new file mode 100644
index 0000000..0b893c5
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/imagesuper_utils.py
@@ -0,0 +1,34 @@
+# 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 torch
+from diffusers import StableDiffusionUpscalePipeline
+
+class Image_Super_Net():
+    def __init__(self, config):
+        self.up_pipeline_x4 = StableDiffusionUpscalePipeline.from_pretrained(
+                        'stabilityai/stable-diffusion-x4-upscaler',
+                        torch_dtype=torch.float16,
+                    ).to(config.device)
+        self.up_pipeline_x4.set_progress_bar_config(disable=True)
+
+    def __call__(self, image, prompt=''):
+        with torch.no_grad():
+            upscaled_image = self.up_pipeline_x4(
+                prompt=[prompt],
+                image=image,
+                num_inference_steps=5,
+            ).images[0]
+
+        return upscaled_image
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py
index de10b6b..4d6a6ba 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/multiview_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -27,8 +17,9 @@ import random
 
 import numpy as np
 import torch
+from typing import List
 from diffusers import DiffusionPipeline
-from diffusers import EulerAncestralDiscreteScheduler
+from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler
 
 
 class Multiview_Diffusion_Net():
@@ -44,8 +35,14 @@ class Multiview_Diffusion_Net():
             multiview_ckpt_path,
             custom_pipeline=custom_pipeline_path, torch_dtype=torch.float16)
 
-        pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config,
-                                                                         timestep_spacing='trailing')
+        if config.pipe_name in ['hunyuanpaint']:
+            pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config,
+                                                                             timestep_spacing='trailing')
+        elif config.pipe_name in ['hunyuanpaint-turbo']:
+            pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config,
+                                                        timestep_spacing='trailing')
+            pipeline.set_turbo(True)
+            # pipeline.prepare() 
 
         pipeline.set_progress_bar_config(disable=True)
         self.pipeline = pipeline.to(self.device)
@@ -56,11 +53,14 @@ class Multiview_Diffusion_Net():
         torch.manual_seed(seed)
         os.environ["PL_GLOBAL_SEED"] = str(seed)
 
-    def __call__(self, input_image, control_images, camera_info):
+    def __call__(self, input_images, control_images, camera_info):
 
         self.seed_everything(0)
 
-        input_image = input_image.resize((self.view_size, self.view_size))
+        if not isinstance(input_images, List):
+            input_images = [input_images]
+
+        input_images = [input_image.resize((self.view_size, self.view_size)) for input_image in input_images]
         for i in range(len(control_images)):
             control_images[i] = control_images[i].resize((self.view_size, self.view_size))
             if control_images[i].mode == 'L':
@@ -82,5 +82,6 @@ class Multiview_Diffusion_Net():
         kwargs["normal_imgs"] = normal_image
         kwargs["position_imgs"] = position_image
 
-        mvd_image = self.pipeline(input_image, num_inference_steps=30, **kwargs).images
+        mvd_image = self.pipeline(input_images, num_inference_steps=30, **kwargs).images
+
         return mvd_image
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py
index 915284d..5c23999 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/simplify_mesh_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py
index 00d2a5d..f55a924 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/texgen/utils/uv_warp_utils.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
@@ -30,6 +20,9 @@ def mesh_uv_wrap(mesh):
     if isinstance(mesh, trimesh.Scene):
         mesh = mesh.dump(concatenate=True)
 
+    if len(mesh.faces) > 500000000:
+        raise ValueError("The mesh has more than 500,000,000 faces, which is not supported.")
+
     vmapping, indices, uvs = xatlas.parametrize(mesh.vertices, mesh.faces)
 
     mesh.vertices = mesh.vertices[vmapping]
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py b/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py
index 405833e..2c8a3ab 100644
--- a/Gen_3D_Modules/Hunyuan3D_V2/hy3dgen/text2image.py
+++ b/Gen_3D_Modules/Hunyuan3D_V2/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
 
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/requirements.txt b/Gen_3D_Modules/Hunyuan3D_V2/requirements.txt
new file mode 100644
index 0000000..abdab84
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/requirements.txt
@@ -0,0 +1,40 @@
+ninja
+pybind11
+
+diffusers
+einops
+opencv-python
+numpy
+torch
+transformers
+torchvision
+#taming-transformers-rom1504
+#ConfigArgParse
+#ipdb
+omegaconf
+
+#sentencepiece
+tqdm
+
+# Mesh Processing
+trimesh
+pymeshlab
+pygltflib
+xatlas
+#kornia
+#facexlib
+
+# Training
+accelerate
+#pytorch_lightning
+#scikit-learn
+#scikit-image
+
+# Demo only
+gradio
+fastapi
+uvicorn
+rembg
+onnxruntime
+#gevent
+#geventhttpclient
\ No newline at end of file
diff --git a/Gen_3D_Modules/Hunyuan3D_V2/setup.py b/Gen_3D_Modules/Hunyuan3D_V2/setup.py
new file mode 100644
index 0000000..5339da1
--- /dev/null
+++ b/Gen_3D_Modules/Hunyuan3D_V2/setup.py
@@ -0,0 +1,46 @@
+# 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, find_packages
+
+setup(
+    name="hy3dgen",
+    version="2.0.2",
+    url="https://github.com/Tencent/Hunyuan3D-2",
+    packages=find_packages(),
+    include_package_data=True,
+    package_data={"hy3dgen": ["assets/*", "assets/**/*"]},
+    install_requires=[
+        'gradio',
+        "tqdm>=4.66.3",
+        'numpy',
+        'ninja',
+        'diffusers',
+        'pybind11',
+        'opencv-python',
+        'einops',
+        "transformers>=4.48.0",
+        'omegaconf',
+        'trimesh',
+        'pymeshlab',
+        'pygltflib',
+        'xatlas',
+        'accelerate',
+        'gradio',
+        'fastapi',
+        'uvicorn',
+        'rembg',
+        'onnxruntime'
+    ]
+)
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/__init__.py b/Gen_3D_Modules/MV_Adapter/mvadapter/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/loaders/__init__.py b/Gen_3D_Modules/MV_Adapter/mvadapter/loaders/__init__.py
new file mode 100644
index 0000000..f93ecab
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/loaders/__init__.py
@@ -0,0 +1 @@
+from .custom_adapter import CustomAdapterMixin
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/loaders/custom_adapter.py b/Gen_3D_Modules/MV_Adapter/mvadapter/loaders/custom_adapter.py
new file mode 100644
index 0000000..e600e4f
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/loaders/custom_adapter.py
@@ -0,0 +1,132 @@
+import os
+from typing import Dict, Optional, Union
+
+import safetensors
+import torch
+from diffusers.utils import _get_model_file, logging
+from safetensors import safe_open
+from huggingface_hub import snapshot_download
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class CustomAdapterMixin:
+    def init_custom_adapter(self, *args, **kwargs):
+        self._init_custom_adapter(*args, **kwargs)
+
+    def _init_custom_adapter(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def load_custom_adapter(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        weight_name: str,
+        subfolder: Optional[str] = None,
+        local_cache_dir: Optional[str] = None,
+        **kwargs,
+    ):
+        # Load the main state dict first.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+            # First try to load from local cache if provided
+            if local_cache_dir and os.path.exists(os.path.join(local_cache_dir, weight_name)):
+                model_file = os.path.join(local_cache_dir, weight_name)
+                logger.info(f"Loading adapter from local cache: {model_file}")
+            else:
+                try:
+                    model_file = _get_model_file(
+                        pretrained_model_name_or_path_or_dict,
+                        weights_name=weight_name,
+                        subfolder=subfolder,
+                        cache_dir=cache_dir,
+                        force_download=force_download,
+                        proxies=proxies,
+                        local_files_only=local_files_only,
+                        token=token,
+                        revision=revision,
+                        user_agent=user_agent,
+                    )
+                except (OSError, EnvironmentError) as e:
+                    # If file not found and we have a custom cache dir, try to download
+                    if local_cache_dir:
+                        logger.info(f"Adapter not found. Downloading {pretrained_model_name_or_path_or_dict} to {local_cache_dir}")
+                        try:
+                            # Create directory if it doesn't exist
+                            os.makedirs(local_cache_dir, exist_ok=True)
+                            
+                            # Download the repository to local cache
+                            snapshot_download(
+                                repo_id=pretrained_model_name_or_path_or_dict,
+                                local_dir=local_cache_dir,
+                                ignore_patterns=["*.yaml", "*.json", "*.py", ".png", ".jpg", ".gif"]
+                            )
+                            
+                            # Now try to load from the local cache
+                            model_file = os.path.join(local_cache_dir, weight_name)
+                            if not os.path.exists(model_file):
+                                raise EnvironmentError(f"File {weight_name} not found even after download to {local_cache_dir}")
+                            
+                        except Exception as download_error:
+                            logger.error(f"Failed to download adapter: {download_error}")
+                            raise e  # Re-raise original error
+                    else:
+                        raise e  # Re-raise original error
+                
+            if weight_name.endswith(".safetensors"):
+                state_dict = {}
+                with safe_open(model_file, framework="pt", device="cpu") as f:
+                    for key in f.keys():
+                        state_dict[key] = f.get_tensor(key)
+            else:
+                state_dict = torch.load(model_file, map_location="cpu")
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        self._load_custom_adapter(state_dict)
+
+    def _load_custom_adapter(self, state_dict):
+        raise NotImplementedError
+
+    def save_custom_adapter(
+        self,
+        save_directory: Union[str, os.PathLike],
+        weight_name: str,
+        safe_serialization: bool = False,
+        **kwargs,
+    ):
+        if os.path.isfile(save_directory):
+            logger.error(
+                f"Provided path ({save_directory}) should be a directory, not a file"
+            )
+            return
+
+        if safe_serialization:
+
+            def save_function(weights, filename):
+                return safetensors.torch.save_file(
+                    weights, filename, metadata={"format": "pt"}
+                )
+
+        else:
+            save_function = torch.save
+
+        # Save the model
+        state_dict = self._save_custom_adapter(**kwargs)
+        save_function(state_dict, os.path.join(save_directory, weight_name))
+        logger.info(
+            f"Custom adapter weights saved in {os.path.join(save_directory, weight_name)}"
+        )
+
+    def _save_custom_adapter(self):
+        raise NotImplementedError
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/models/__init__.py b/Gen_3D_Modules/MV_Adapter/mvadapter/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/models/attention_processor.py b/Gen_3D_Modules/MV_Adapter/mvadapter/models/attention_processor.py
new file mode 100644
index 0000000..55eebb8
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/models/attention_processor.py
@@ -0,0 +1,743 @@
+import math
+from typing import Callable, List, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from diffusers.models.attention_processor import Attention
+from diffusers.models.unets import UNet2DConditionModel
+from diffusers.utils import deprecate, logging
+from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available
+from einops import rearrange, repeat
+from torch import nn
+
+
+def default_set_attn_proc_func(
+    name: str,
+    hidden_size: int,
+    cross_attention_dim: Optional[int],
+    ori_attn_proc: object,
+) -> object:
+    return ori_attn_proc
+
+
+def set_unet_2d_condition_attn_processor(
+    unet: UNet2DConditionModel,
+    set_self_attn_proc_func: Callable = default_set_attn_proc_func,
+    set_cross_attn_proc_func: Callable = default_set_attn_proc_func,
+    set_custom_attn_proc_func: Callable = default_set_attn_proc_func,
+    set_self_attn_module_names: Optional[List[str]] = None,
+    set_cross_attn_module_names: Optional[List[str]] = None,
+    set_custom_attn_module_names: Optional[List[str]] = None,
+) -> None:
+    do_set_processor = lambda name, module_names: (
+        any([name.startswith(module_name) for module_name in module_names])
+        if module_names is not None
+        else True
+    )  # prefix match
+
+    attn_procs = {}
+    for name, attn_processor in unet.attn_processors.items():
+        # set attn_processor by default, if module_names is None
+        set_self_attn_processor = do_set_processor(name, set_self_attn_module_names)
+        set_cross_attn_processor = do_set_processor(name, set_cross_attn_module_names)
+        set_custom_attn_processor = do_set_processor(name, set_custom_attn_module_names)
+
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+
+        is_custom = "attn_mid_blocks" in name or "attn_post_blocks" in name
+        if is_custom:
+            attn_procs[name] = (
+                set_custom_attn_proc_func(name, hidden_size, None, attn_processor)
+                if set_custom_attn_processor
+                else attn_processor
+            )
+        else:
+            cross_attention_dim = (
+                None
+                if name.endswith("attn1.processor")
+                else unet.config.cross_attention_dim
+            )
+            if cross_attention_dim is None or "motion_modules" in name:
+                # self attention
+                attn_procs[name] = (
+                    set_self_attn_proc_func(
+                        name, hidden_size, cross_attention_dim, attn_processor
+                    )
+                    if set_self_attn_processor
+                    else attn_processor
+                )
+            else:
+                # cross attention
+                attn_procs[name] = (
+                    set_cross_attn_proc_func(
+                        name, hidden_size, cross_attention_dim, attn_processor
+                    )
+                    if set_cross_attn_processor
+                    else attn_processor
+                )
+
+    unet.set_attn_processor(attn_procs)
+
+
+class DecoupledMVRowSelfAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for Decoupled Row-wise Self-Attention and Image Cross-Attention for PyTorch 2.0.
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        inner_dim: int,
+        num_views: int = 1,
+        name: Optional[str] = None,
+        use_mv: bool = True,
+        use_ref: bool = False,
+    ):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "DecoupledMVRowSelfAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+        super().__init__()
+
+        self.num_views = num_views
+        self.name = name  # NOTE: need for image cross-attention
+        self.use_mv = use_mv
+        self.use_ref = use_ref
+
+        if self.use_mv:
+            self.to_q_mv = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_k_mv = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_v_mv = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_out_mv = nn.ModuleList(
+                [
+                    nn.Linear(in_features=inner_dim, out_features=query_dim, bias=True),
+                    nn.Dropout(0.0),
+                ]
+            )
+
+        if self.use_ref:
+            self.to_q_ref = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_k_ref = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_v_ref = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_out_ref = nn.ModuleList(
+                [
+                    nn.Linear(in_features=inner_dim, out_features=query_dim, bias=True),
+                    nn.Dropout(0.0),
+                ]
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        mv_scale: float = 1.0,
+        ref_hidden_states: Optional[torch.FloatTensor] = None,
+        ref_scale: float = 1.0,
+        cache_hidden_states: Optional[List[torch.FloatTensor]] = None,
+        use_mv: bool = True,
+        use_ref: bool = True,
+        num_views: Optional[int] = None,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        """
+        New args:
+            mv_scale (float): scale for multi-view self-attention.
+            ref_hidden_states (torch.FloatTensor): reference encoder hidden states for image cross-attention.
+            ref_scale (float): scale for image cross-attention.
+            cache_hidden_states (List[torch.FloatTensor]): cache hidden states from reference unet.
+
+        """
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            deprecate("scale", "1.0.0", deprecation_message)
+
+        if num_views is not None:
+            self.num_views = num_views
+
+        # NEW: cache hidden states for reference unet
+        if cache_hidden_states is not None:
+            cache_hidden_states[self.name] = hidden_states.clone()
+
+        # NEW: whether to use multi-view attention and image cross-attention
+        use_mv = self.use_mv and use_mv
+        use_ref = self.use_ref and use_ref
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        # NEW: for decoupled multi-view attention
+        if use_mv:
+            query_mv = self.to_q_mv(hidden_states)
+
+        # NEW: for decoupled reference cross attention
+        if use_ref:
+            query_ref = self.to_q_ref(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        ####### Decoupled multi-view self-attention ########
+        if use_mv:
+            key_mv = self.to_k_mv(encoder_hidden_states)
+            value_mv = self.to_v_mv(encoder_hidden_states)
+
+            query_mv = query_mv.view(batch_size, -1, attn.heads, head_dim)
+            key_mv = key_mv.view(batch_size, -1, attn.heads, head_dim)
+            value_mv = value_mv.view(batch_size, -1, attn.heads, head_dim)
+
+            height = width = math.isqrt(sequence_length)
+
+            # row self-attention
+            query_mv = rearrange(
+                query_mv,
+                "(b nv) (ih iw) h c -> (b nv ih) iw h c",
+                nv=self.num_views,
+                ih=height,
+                iw=width,
+            ).transpose(1, 2)
+            key_mv = rearrange(
+                key_mv,
+                "(b nv) (ih iw) h c -> b ih (nv iw) h c",
+                nv=self.num_views,
+                ih=height,
+                iw=width,
+            )
+            key_mv = (
+                key_mv.repeat_interleave(self.num_views, dim=0)
+                .view(batch_size * height, -1, attn.heads, head_dim)
+                .transpose(1, 2)
+            )
+            value_mv = rearrange(
+                value_mv,
+                "(b nv) (ih iw) h c -> b ih (nv iw) h c",
+                nv=self.num_views,
+                ih=height,
+                iw=width,
+            )
+            value_mv = (
+                value_mv.repeat_interleave(self.num_views, dim=0)
+                .view(batch_size * height, -1, attn.heads, head_dim)
+                .transpose(1, 2)
+            )
+
+            hidden_states_mv = F.scaled_dot_product_attention(
+                query_mv,
+                key_mv,
+                value_mv,
+                dropout_p=0.0,
+                is_causal=False,
+            )
+            hidden_states_mv = rearrange(
+                hidden_states_mv,
+                "(b nv ih) h iw c -> (b nv) (ih iw) (h c)",
+                nv=self.num_views,
+                ih=height,
+            )
+            hidden_states_mv = hidden_states_mv.to(query.dtype)
+
+            # linear proj
+            hidden_states_mv = self.to_out_mv[0](hidden_states_mv)
+            # dropout
+            hidden_states_mv = self.to_out_mv[1](hidden_states_mv)
+
+        if use_ref:
+            reference_hidden_states = ref_hidden_states[self.name]
+
+            key_ref = self.to_k_ref(reference_hidden_states)
+            value_ref = self.to_v_ref(reference_hidden_states)
+
+            query_ref = query_ref.view(batch_size, -1, attn.heads, head_dim).transpose(
+                1, 2
+            )
+            key_ref = key_ref.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+            value_ref = value_ref.view(batch_size, -1, attn.heads, head_dim).transpose(
+                1, 2
+            )
+
+            hidden_states_ref = F.scaled_dot_product_attention(
+                query_ref, key_ref, value_ref, dropout_p=0.0, is_causal=False
+            )
+
+            hidden_states_ref = hidden_states_ref.transpose(1, 2).reshape(
+                batch_size, -1, attn.heads * head_dim
+            )
+            hidden_states_ref = hidden_states_ref.to(query.dtype)
+
+            # linear proj
+            hidden_states_ref = self.to_out_ref[0](hidden_states_ref)
+            # dropout
+            hidden_states_ref = self.to_out_ref[1](hidden_states_ref)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if use_mv:
+            hidden_states = hidden_states + hidden_states_mv * mv_scale
+
+        if use_ref:
+            hidden_states = hidden_states + hidden_states_ref * ref_scale
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+    def set_num_views(self, num_views: int) -> None:
+        self.num_views = num_views
+
+
+class DecoupledMVRowColSelfAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for Decoupled Row-wise Self-Attention and Image Cross-Attention for PyTorch 2.0.
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        inner_dim: int,
+        num_views: int = 1,
+        name: Optional[str] = None,
+        use_mv: bool = True,
+        use_ref: bool = False,
+    ):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "DecoupledMVRowSelfAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+        super().__init__()
+
+        self.num_views = num_views
+        self.name = name  # NOTE: need for image cross-attention
+        self.use_mv = use_mv
+        self.use_ref = use_ref
+
+        if self.use_mv:
+            self.to_q_mv = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_k_mv = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_v_mv = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_out_mv = nn.ModuleList(
+                [
+                    nn.Linear(in_features=inner_dim, out_features=query_dim, bias=True),
+                    nn.Dropout(0.0),
+                ]
+            )
+
+        if self.use_ref:
+            self.to_q_ref = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_k_ref = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_v_ref = nn.Linear(
+                in_features=query_dim, out_features=inner_dim, bias=False
+            )
+            self.to_out_ref = nn.ModuleList(
+                [
+                    nn.Linear(in_features=inner_dim, out_features=query_dim, bias=True),
+                    nn.Dropout(0.0),
+                ]
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        mv_scale: float = 1.0,
+        ref_hidden_states: Optional[torch.FloatTensor] = None,
+        ref_scale: float = 1.0,
+        cache_hidden_states: Optional[List[torch.FloatTensor]] = None,
+        use_mv: bool = True,
+        use_ref: bool = True,
+        num_views: Optional[int] = None,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        """
+        New args:
+            mv_scale (float): scale for multi-view self-attention.
+            ref_hidden_states (torch.FloatTensor): reference encoder hidden states for image cross-attention.
+            ref_scale (float): scale for image cross-attention.
+            cache_hidden_states (List[torch.FloatTensor]): cache hidden states from reference unet.
+
+        """
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            deprecate("scale", "1.0.0", deprecation_message)
+
+        if num_views is not None:
+            self.num_views = num_views
+
+        # NEW: cache hidden states for reference unet
+        if cache_hidden_states is not None:
+            cache_hidden_states[self.name] = hidden_states.clone()
+
+        # NEW: whether to use multi-view attention and image cross-attention
+        use_mv = self.use_mv and use_mv
+        use_ref = self.use_ref and use_ref
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        # NEW: for decoupled multi-view attention
+        if use_mv:
+            query_mv = self.to_q_mv(hidden_states)
+
+        # NEW: for decoupled reference cross attention
+        if use_ref:
+            query_ref = self.to_q_ref(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        ####### Decoupled multi-view self-attention ########
+        if use_mv:
+            key_mv = self.to_k_mv(encoder_hidden_states)
+            value_mv = self.to_v_mv(encoder_hidden_states)
+
+            query_mv = query_mv.view(batch_size, -1, attn.heads, head_dim)
+            key_mv = key_mv.view(batch_size, -1, attn.heads, head_dim)
+            value_mv = value_mv.view(batch_size, -1, attn.heads, head_dim)
+
+            height = width = math.isqrt(sequence_length)
+
+            query_mv = rearrange(
+                query_mv,
+                "(b nv) (ih iw) h c -> b nv ih iw h c",
+                nv=self.num_views,
+                ih=height,
+                iw=width,
+            )
+            key_mv = rearrange(
+                key_mv,
+                "(b nv) (ih iw) h c -> b nv ih iw h c",
+                nv=self.num_views,
+                ih=height,
+                iw=width,
+            )
+            value_mv = rearrange(
+                value_mv,
+                "(b nv) (ih iw) h c -> b nv ih iw h c",
+                nv=self.num_views,
+                ih=height,
+                iw=width,
+            )
+
+            # row-wise attention for view 0123 (front, right, back, left)
+            query_mv_0123 = rearrange(
+                query_mv[:, 0:4], "b nv ih iw h c -> (b ih) h (nv iw) c"
+            )
+            key_mv_0123 = rearrange(
+                key_mv[:, 0:4], "b nv ih iw h c -> (b ih) h (nv iw) c"
+            )
+            value_mv_0123 = rearrange(
+                value_mv[:, 0:4], "b nv ih iw h c -> (b ih) h (nv iw) c"
+            )
+            hidden_states_mv_0123 = F.scaled_dot_product_attention(
+                query_mv_0123,
+                key_mv_0123,
+                value_mv_0123,
+                dropout_p=0.0,
+                is_causal=False,
+            )
+            hidden_states_mv_0123 = rearrange(
+                hidden_states_mv_0123,
+                "(b ih) h (nv iw) c -> b nv (ih iw) (h c)",
+                ih=height,
+                iw=height,
+            )
+
+            # col-wise attention for view 0245 (front, back, top, bottom)
+            # flip first
+            query_mv_0245 = torch.cat(
+                [
+                    torch.flip(query_mv[:, [0]], [3]),  # horizontal flip
+                    query_mv[:, [2, 4, 5]],
+                ],
+                dim=1,
+            )
+            key_mv_0245 = torch.cat(
+                [
+                    torch.flip(key_mv[:, [0]], [3]),  # horizontal flip
+                    key_mv[:, [2, 4, 5]],
+                ],
+                dim=1,
+            )
+            value_mv_0245 = torch.cat(
+                [
+                    torch.flip(value_mv[:, [0]], [3]),  # horizontal flip
+                    value_mv[:, [2, 4, 5]],
+                ],
+                dim=1,
+            )
+            # attention
+            query_mv_0245 = rearrange(
+                query_mv_0245, "b nv ih iw h c -> (b iw) h (nv ih) c"
+            )
+            key_mv_0245 = rearrange(key_mv_0245, "b nv ih iw h c -> (b iw) h (nv ih) c")
+            value_mv_0245 = rearrange(
+                value_mv_0245, "b nv ih iw h c -> (b iw) h (nv ih) c"
+            )
+            hidden_states_mv_0245 = F.scaled_dot_product_attention(
+                query_mv_0245,
+                key_mv_0245,
+                value_mv_0245,
+                dropout_p=0.0,
+                is_causal=False,
+            )
+            # flip back
+            hidden_states_mv_0245 = rearrange(
+                hidden_states_mv_0245,
+                "(b iw) h (nv ih) c -> b nv ih iw (h c)",
+                ih=height,
+                iw=height,
+            )
+            hidden_states_mv_0245 = torch.cat(
+                [
+                    torch.flip(hidden_states_mv_0245[:, [0]], [3]),  # horizontal flip
+                    hidden_states_mv_0245[:, [1, 2, 3]],
+                ],
+                dim=1,
+            )
+            hidden_states_mv_0245 = hidden_states_mv_0245.view(
+                hidden_states_mv_0245.shape[0],
+                hidden_states_mv_0245.shape[1],
+                -1,
+                hidden_states_mv_0245.shape[-1],
+            )
+
+            # combine row and col
+            hidden_states_mv = torch.stack(
+                [
+                    (hidden_states_mv_0123[:, 0] + hidden_states_mv_0245[:, 0]) / 2,
+                    hidden_states_mv_0123[:, 1],
+                    (hidden_states_mv_0123[:, 2] + hidden_states_mv_0245[:, 1]) / 2,
+                    hidden_states_mv_0123[:, 3],
+                    hidden_states_mv_0245[:, 2],
+                    hidden_states_mv_0245[:, 3],
+                ],
+                dim=1,
+            )
+
+            hidden_states_mv = hidden_states_mv.view(
+                -1, hidden_states_mv.shape[-2], hidden_states_mv.shape[-1]
+            )
+            hidden_states_mv = hidden_states_mv.to(query.dtype)
+
+            # linear proj
+            hidden_states_mv = self.to_out_mv[0](hidden_states_mv)
+            # dropout
+            hidden_states_mv = self.to_out_mv[1](hidden_states_mv)
+
+        if use_ref:
+            reference_hidden_states = ref_hidden_states[self.name]
+
+            key_ref = self.to_k_ref(reference_hidden_states)
+            value_ref = self.to_v_ref(reference_hidden_states)
+
+            query_ref = query_ref.view(batch_size, -1, attn.heads, head_dim).transpose(
+                1, 2
+            )
+            key_ref = key_ref.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+            value_ref = value_ref.view(batch_size, -1, attn.heads, head_dim).transpose(
+                1, 2
+            )
+
+            hidden_states_ref = F.scaled_dot_product_attention(
+                query_ref, key_ref, value_ref, dropout_p=0.0, is_causal=False
+            )
+
+            hidden_states_ref = hidden_states_ref.transpose(1, 2).reshape(
+                batch_size, -1, attn.heads * head_dim
+            )
+            hidden_states_ref = hidden_states_ref.to(query.dtype)
+
+            # linear proj
+            hidden_states_ref = self.to_out_ref[0](hidden_states_ref)
+            # dropout
+            hidden_states_ref = self.to_out_ref[1](hidden_states_ref)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if use_mv:
+            hidden_states = hidden_states + hidden_states_mv * mv_scale
+
+        if use_ref:
+            hidden_states = hidden_states + hidden_states_ref * ref_scale
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+    def set_num_views(self, num_views: int) -> None:
+        self.num_views = num_views
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_mvadapter_i2mv_sdxl.py b/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_mvadapter_i2mv_sdxl.py
new file mode 100644
index 0000000..79d1128
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_mvadapter_i2mv_sdxl.py
@@ -0,0 +1,962 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL
+import torch
+import torch.nn as nn
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.models import (
+    AutoencoderKL,
+    ImageProjection,
+    T2IAdapter,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import (
+    StableDiffusionXLPipelineOutput,
+)
+from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
+    StableDiffusionXLPipeline,
+    rescale_noise_cfg,
+    retrieve_timesteps,
+)
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import deprecate, logging
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from ..loaders import CustomAdapterMixin
+from ..models.attention_processor import (
+    DecoupledMVRowSelfAttnProcessor2_0,
+    set_unet_2d_condition_attn_processor,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def retrieve_latents(
+    encoder_output: torch.Tensor,
+    generator: Optional[torch.Generator] = None,
+    sample_mode: str = "sample",
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class MVAdapterI2MVSDXLPipeline(StableDiffusionXLPipeline, CustomAdapterMixin):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        feature_extractor: CLIPImageProcessor = None,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+    ):
+        super().__init__(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            scheduler=scheduler,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+            force_zeros_for_empty_prompt=force_zeros_for_empty_prompt,
+            add_watermarker=add_watermarker,
+        )
+
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor,
+            do_convert_rgb=True,
+            do_normalize=False,
+        )
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.prepare_latents
+    def prepare_image_latents(
+        self,
+        image,
+        timestep,
+        batch_size,
+        num_images_per_prompt,
+        dtype,
+        device,
+        generator=None,
+        add_noise=True,
+    ):
+        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+
+        latents_mean = latents_std = None
+        if (
+            hasattr(self.vae.config, "latents_mean")
+            and self.vae.config.latents_mean is not None
+        ):
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if (
+            hasattr(self.vae.config, "latents_std")
+            and self.vae.config.latents_std is not None
+        ):
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
+        # Offload text encoder if `enable_model_cpu_offload` was enabled
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.text_encoder_2.to("cpu")
+            torch.cuda.empty_cache()
+
+        image = image.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+
+        if image.shape[1] == 4:
+            init_latents = image
+
+        else:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            if self.vae.config.force_upcast:
+                image = image.float()
+                self.vae.to(dtype=torch.float32)
+
+            if isinstance(generator, list) and len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+
+            elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
+                init_latents = [
+                    retrieve_latents(
+                        self.vae.encode(image[i : i + 1]), generator=generator[i]
+                    )
+                    for i in range(batch_size)
+                ]
+                init_latents = torch.cat(init_latents, dim=0)
+            else:
+                init_latents = retrieve_latents(
+                    self.vae.encode(image), generator=generator
+                )
+
+            if self.vae.config.force_upcast:
+                self.vae.to(dtype)
+
+            init_latents = init_latents.to(dtype)
+            if latents_mean is not None and latents_std is not None:
+                latents_mean = latents_mean.to(device=device, dtype=dtype)
+                latents_std = latents_std.to(device=device, dtype=dtype)
+                init_latents = (
+                    (init_latents - latents_mean)
+                    * self.vae.config.scaling_factor
+                    / latents_std
+                )
+            else:
+                init_latents = self.vae.config.scaling_factor * init_latents
+
+        if (
+            batch_size > init_latents.shape[0]
+            and batch_size % init_latents.shape[0] == 0
+        ):
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat(
+                [init_latents] * additional_image_per_prompt, dim=0
+            )
+        elif (
+            batch_size > init_latents.shape[0]
+            and batch_size % init_latents.shape[0] != 0
+        ):
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        if add_noise:
+            shape = init_latents.shape
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # get latents
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+        latents = init_latents
+
+        return latents
+
+    def prepare_control_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        num_empty_images=0,  # for concat in batch like ImageDream
+    ):
+        assert hasattr(
+            self, "control_image_processor"
+        ), "control_image_processor is not initialized"
+
+        image = self.control_image_processor.preprocess(
+            image, height=height, width=width
+        ).to(dtype=torch.float32)
+
+        if num_empty_images > 0:
+            image = torch.cat(
+                [image, torch.zeros_like(image[:num_empty_images])], dim=0
+            )
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt  # always 1 for control image
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        # NEW
+        mv_scale: float = 1.0,
+        # Camera or geometry condition
+        control_image: Optional[PipelineImageInput] = None,
+        control_conditioning_scale: Optional[float] = 1.0,
+        control_conditioning_factor: float = 1.0,
+        # Image condition
+        reference_image: Optional[PipelineImageInput] = None,
+        reference_conditioning_scale: Optional[float] = 1.0,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        # 0. Default height and width to unet
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None)
+            if self.cross_attention_kwargs is not None
+            else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps
+        )
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat(
+                [negative_pooled_prompt_embeds, add_text_embeds], dim=0
+            )
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(
+            batch_size * num_images_per_prompt, 1
+        )
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # Preprocess reference image
+        reference_image = self.image_processor.preprocess(reference_image)
+        reference_latents = self.prepare_image_latents(
+            reference_image,
+            timesteps[:1].repeat(batch_size * num_images_per_prompt),  # no use
+            batch_size,
+            1,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            add_noise=False,
+        )
+
+        with torch.no_grad():
+            ref_timesteps = torch.zeros_like(timesteps[0])
+            ref_hidden_states = {}
+
+            self.unet(
+                reference_latents,
+                ref_timesteps,
+                encoder_hidden_states=prompt_embeds[-1:],
+                added_cond_kwargs={
+                    "text_embeds": add_text_embeds[-1:],
+                    "time_ids": add_time_ids[-1:],
+                },
+                cross_attention_kwargs={
+                    "cache_hidden_states": ref_hidden_states,
+                    "use_mv": False,
+                    "use_ref": False,
+                },
+                return_dict=False,
+            )
+            ref_hidden_states = {
+                k: v.repeat_interleave(num_images_per_prompt, dim=0)
+                for k, v in ref_hidden_states.items()
+            }
+        if self.do_classifier_free_guidance:
+            ref_hidden_states = {
+                k: torch.cat([torch.zeros_like(v), v], dim=0)
+                for k, v in ref_hidden_states.items()
+            }
+
+        cross_attention_kwargs = {
+            "mv_scale": mv_scale,
+            "ref_hidden_states": {k: v.clone() for k, v in ref_hidden_states.items()},
+            "ref_scale": reference_conditioning_scale,
+            "num_views": num_images_per_prompt,
+            **(self.cross_attention_kwargs or {}),
+        }
+
+        # Preprocess control image
+        control_image_feature = self.prepare_control_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=1,  # NOTE: always 1 for control images
+            device=device,
+            dtype=latents.dtype,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+        )
+        control_image_feature = control_image_feature.to(
+            device=device, dtype=latents.dtype
+        )
+
+        adapter_state = self.cond_encoder(control_image_feature)
+        for i, state in enumerate(adapter_state):
+            adapter_state[i] = state * control_conditioning_scale
+
+        # 8. Denoising loop
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+
+        # 8.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(
+                list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))
+            )
+            timesteps = timesteps[:num_inference_steps]
+
+        # 9. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(
+                batch_size * num_images_per_prompt
+            )
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance
+                    else latents
+                )
+
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                added_cond_kwargs = {
+                    "text_embeds": add_text_embeds,
+                    "time_ids": add_time_ids,
+                }
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+
+                if i < int(num_inference_steps * control_conditioning_factor):
+                    down_intrablock_additional_residuals = [
+                        state.clone() for state in adapter_state
+                    ]
+                else:
+                    down_intrablock_additional_residuals = None
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_intrablock_additional_residuals=down_intrablock_additional_residuals,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(
+                        noise_pred,
+                        noise_pred_text,
+                        guidance_rescale=self.guidance_rescale,
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs, return_dict=False
+                )[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop(
+                        "negative_prompt_embeds", negative_prompt_embeds
+                    )
+                    add_text_embeds = callback_outputs.pop(
+                        "add_text_embeds", add_text_embeds
+                    )
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop(
+                        "negative_add_time_ids", negative_add_time_ids
+                    )
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = (
+                self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            )
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(
+                    next(iter(self.vae.post_quant_conv.parameters())).dtype
+                )
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = (
+                hasattr(self.vae.config, "latents_mean")
+                and self.vae.config.latents_mean is not None
+            )
+            has_latents_std = (
+                hasattr(self.vae.config, "latents_std")
+                and self.vae.config.latents_std is not None
+            )
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean)
+                    .view(1, 4, 1, 1)
+                    .to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std)
+                    .view(1, 4, 1, 1)
+                    .to(latents.device, latents.dtype)
+                )
+                latents = (
+                    latents * latents_std / self.vae.config.scaling_factor
+                    + latents_mean
+                )
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
+
+    ### NEW: adapters ###
+    def _init_custom_adapter(
+        self,
+        # Multi-view adapter
+        num_views: int = 1,
+        self_attn_processor: Any = DecoupledMVRowSelfAttnProcessor2_0,
+        # Condition encoder
+        cond_in_channels: int = 6,
+        # For training
+        copy_attn_weights: bool = True,
+        zero_init_module_keys: List[str] = [],
+    ):
+        # Condition encoder
+        self.cond_encoder = T2IAdapter(
+            in_channels=cond_in_channels,
+            channels=(320, 640, 1280, 1280),
+            num_res_blocks=2,
+            downscale_factor=16,
+            adapter_type="full_adapter_xl",
+        )
+
+        # set custom attn processor for multi-view attention and image cross-attention
+        self.unet: UNet2DConditionModel
+        set_unet_2d_condition_attn_processor(
+            self.unet,
+            set_self_attn_proc_func=lambda name, hs, cad, ap: self_attn_processor(
+                query_dim=hs,
+                inner_dim=hs,
+                num_views=num_views,
+                name=name,
+                use_mv=True,
+                use_ref=True,
+            ),
+            set_cross_attn_proc_func=lambda name, hs, cad, ap: self_attn_processor(
+                query_dim=hs,
+                inner_dim=hs,
+                num_views=num_views,
+                name=name,
+                use_mv=False,
+                use_ref=False,
+            ),
+        )
+
+        # copy decoupled attention weights from original unet
+        if copy_attn_weights:
+            state_dict = self.unet.state_dict()
+            for key in state_dict.keys():
+                if "_mv" in key:
+                    compatible_key = key.replace("_mv", "").replace("processor.", "")
+                elif "_ref" in key:
+                    compatible_key = key.replace("_ref", "").replace("processor.", "")
+                else:
+                    compatible_key = key
+
+                is_zero_init_key = any([k in key for k in zero_init_module_keys])
+                if is_zero_init_key:
+                    state_dict[key] = torch.zeros_like(state_dict[compatible_key])
+                else:
+                    state_dict[key] = state_dict[compatible_key].clone()
+            self.unet.load_state_dict(state_dict)
+
+    def _load_custom_adapter(self, state_dict):
+        self.unet.load_state_dict(state_dict, strict=False)
+        self.cond_encoder.load_state_dict(state_dict, strict=False)
+
+    def _save_custom_adapter(
+        self,
+        include_keys: Optional[List[str]] = None,
+        exclude_keys: Optional[List[str]] = None,
+    ):
+        def include_fn(k):
+            is_included = False
+
+            if include_keys is not None:
+                is_included = is_included or any([key in k for key in include_keys])
+            if exclude_keys is not None:
+                is_included = is_included and not any(
+                    [key in k for key in exclude_keys]
+                )
+
+            return is_included
+
+        state_dict = {k: v for k, v in self.unet.state_dict().items() if include_fn(k)}
+        state_dict.update(self.cond_encoder.state_dict())
+
+        return state_dict
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_mvadapter_t2mv_sdxl.py b/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_mvadapter_t2mv_sdxl.py
new file mode 100644
index 0000000..cae6bc6
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_mvadapter_t2mv_sdxl.py
@@ -0,0 +1,801 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.models import AutoencoderKL, T2IAdapter, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import (
+    StableDiffusionXLPipelineOutput,
+)
+from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
+    StableDiffusionXLPipeline,
+    rescale_noise_cfg,
+    retrieve_timesteps,
+)
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import deprecate, logging
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from ..loaders import CustomAdapterMixin
+from ..models.attention_processor import (
+    DecoupledMVRowSelfAttnProcessor2_0,
+    set_unet_2d_condition_attn_processor,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class MVAdapterT2MVSDXLPipeline(StableDiffusionXLPipeline, CustomAdapterMixin):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        feature_extractor: CLIPImageProcessor = None,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+    ):
+        super().__init__(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            scheduler=scheduler,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+            force_zeros_for_empty_prompt=force_zeros_for_empty_prompt,
+            add_watermarker=add_watermarker,
+        )
+
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor,
+            do_convert_rgb=True,
+            do_normalize=False,
+        )
+
+    def prepare_control_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+    ):
+        assert hasattr(
+            self, "control_image_processor"
+        ), "control_image_processor is not initialized"
+
+        image = self.control_image_processor.preprocess(
+            image, height=height, width=width
+        ).to(dtype=torch.float32)
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt  # always 1 for control image
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        # NEW
+        mv_scale: float = 1.0,
+        # Camera or geometry condition
+        control_image: Optional[PipelineImageInput] = None,
+        control_conditioning_scale: Optional[float] = 1.0,
+        control_conditioning_factor: float = 1.0,
+        # Optional. controlnet
+        controlnet_image: Optional[PipelineImageInput] = None,
+        controlnet_conditioning_scale: Optional[float] = 1.0,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        # 0. Default height and width to unet
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None)
+            if self.cross_attention_kwargs is not None
+            else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps
+        )
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat(
+                [negative_pooled_prompt_embeds, add_text_embeds], dim=0
+            )
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(
+            batch_size * num_images_per_prompt, 1
+        )
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # Preprocess control image
+        control_image_feature = self.prepare_control_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=1,  # NOTE: always 1 for control images
+            device=device,
+            dtype=latents.dtype,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+        )
+        control_image_feature = control_image_feature.to(
+            device=device, dtype=latents.dtype
+        )
+
+        adapter_state = self.cond_encoder(control_image_feature)
+        for i, state in enumerate(adapter_state):
+            adapter_state[i] = state * control_conditioning_scale
+
+        # Preprocess controlnet image if provided
+        do_controlnet = controlnet_image is not None and hasattr(self, "controlnet")
+        if do_controlnet:
+            controlnet_image = self.prepare_control_image(
+                image=controlnet_image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=1,  # NOTE: always 1 for control images
+                device=device,
+                dtype=latents.dtype,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+            )
+            controlnet_image = controlnet_image.to(device=device, dtype=latents.dtype)
+
+        # 8. Denoising loop
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+
+        # 8.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(
+                list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))
+            )
+            timesteps = timesteps[:num_inference_steps]
+
+        # 9. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(
+                batch_size * num_images_per_prompt
+            )
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance
+                    else latents
+                )
+
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                added_cond_kwargs = {
+                    "text_embeds": add_text_embeds,
+                    "time_ids": add_time_ids,
+                }
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+
+                if i < int(num_inference_steps * control_conditioning_factor):
+                    down_intrablock_additional_residuals = [
+                        state.clone() for state in adapter_state
+                    ]
+                else:
+                    down_intrablock_additional_residuals = None
+
+                unet_add_kwargs = {}
+
+                # Do controlnet if provided
+                if do_controlnet:
+                    down_block_res_samples, mid_block_res_sample = self.controlnet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        controlnet_cond=controlnet_image,
+                        conditioning_scale=controlnet_conditioning_scale,
+                        guess_mode=False,
+                        added_cond_kwargs=added_cond_kwargs,
+                        return_dict=False,
+                    )
+                    unet_add_kwargs.update(
+                        {
+                            "down_block_additional_residuals": down_block_res_samples,
+                            "mid_block_additional_residual": mid_block_res_sample,
+                        }
+                    )
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs={
+                        "mv_scale": mv_scale,
+                        "num_views": num_images_per_prompt,
+                        **(self.cross_attention_kwargs or {}),
+                    },
+                    down_intrablock_additional_residuals=down_intrablock_additional_residuals,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                    **unet_add_kwargs,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(
+                        noise_pred,
+                        noise_pred_text,
+                        guidance_rescale=self.guidance_rescale,
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs, return_dict=False
+                )[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop(
+                        "negative_prompt_embeds", negative_prompt_embeds
+                    )
+                    add_text_embeds = callback_outputs.pop(
+                        "add_text_embeds", add_text_embeds
+                    )
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop(
+                        "negative_add_time_ids", negative_add_time_ids
+                    )
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = (
+                self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            )
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(
+                    next(iter(self.vae.post_quant_conv.parameters())).dtype
+                )
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = (
+                hasattr(self.vae.config, "latents_mean")
+                and self.vae.config.latents_mean is not None
+            )
+            has_latents_std = (
+                hasattr(self.vae.config, "latents_std")
+                and self.vae.config.latents_std is not None
+            )
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean)
+                    .view(1, 4, 1, 1)
+                    .to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std)
+                    .view(1, 4, 1, 1)
+                    .to(latents.device, latents.dtype)
+                )
+                latents = (
+                    latents * latents_std / self.vae.config.scaling_factor
+                    + latents_mean
+                )
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
+
+    ### NEW: adapters ###
+    def _init_custom_adapter(
+        self,
+        # Multi-view adapter
+        num_views: int = 1,
+        self_attn_processor: Any = DecoupledMVRowSelfAttnProcessor2_0,
+        # Condition encoder
+        cond_in_channels: int = 6,
+        # For training
+        copy_attn_weights: bool = True,
+        zero_init_module_keys: List[str] = [],
+    ):
+        # Condition encoder
+        self.cond_encoder = T2IAdapter(
+            in_channels=cond_in_channels,
+            channels=(320, 640, 1280, 1280),
+            num_res_blocks=2,
+            downscale_factor=16,
+            adapter_type="full_adapter_xl",
+        )
+
+        # set custom attn processor for multi-view attention
+        self.unet: UNet2DConditionModel
+        set_unet_2d_condition_attn_processor(
+            self.unet,
+            set_self_attn_proc_func=lambda name, hs, cad, ap: self_attn_processor(
+                query_dim=hs,
+                inner_dim=hs,
+                num_views=num_views,
+                name=name,
+                use_mv=True,
+                use_ref=False,
+            ),
+            set_cross_attn_proc_func=lambda name, hs, cad, ap: self_attn_processor(
+                query_dim=hs,
+                inner_dim=hs,
+                num_views=num_views,
+                name=name,
+                use_mv=False,
+                use_ref=False,
+            ),
+        )
+
+        # copy decoupled attention weights from original unet
+        if copy_attn_weights:
+            state_dict = self.unet.state_dict()
+            for key in state_dict.keys():
+                if "_mv" in key:
+                    compatible_key = key.replace("_mv", "").replace("processor.", "")
+                else:
+                    compatible_key = key
+
+                is_zero_init_key = any([k in key for k in zero_init_module_keys])
+                if is_zero_init_key:
+                    state_dict[key] = torch.zeros_like(state_dict[compatible_key])
+                else:
+                    state_dict[key] = state_dict[compatible_key].clone()
+            self.unet.load_state_dict(state_dict)
+
+    def _load_custom_adapter(self, state_dict):
+        self.unet.load_state_dict(state_dict, strict=False)
+        self.cond_encoder.load_state_dict(state_dict, strict=False)
+
+    def _save_custom_adapter(
+        self,
+        include_keys: Optional[List[str]] = None,
+        exclude_keys: Optional[List[str]] = None,
+    ):
+        def include_fn(k):
+            is_included = False
+
+            if include_keys is not None:
+                is_included = is_included or any([key in k for key in include_keys])
+            if exclude_keys is not None:
+                is_included = is_included and not any(
+                    [key in k for key in exclude_keys]
+                )
+
+            return is_included
+
+        state_dict = {k: v for k, v in self.unet.state_dict().items() if include_fn(k)}
+        state_dict.update(self.cond_encoder.state_dict())
+
+        return state_dict
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_texture.py b/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_texture.py
new file mode 100644
index 0000000..a177eb9
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/pipelines/pipeline_texture.py
@@ -0,0 +1,452 @@
+import os
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from spandrel import ModelLoader
+
+from MV_Adapter.mvadapter.utils import image_to_tensor, make_image_grid, tensor_to_image
+from MV_Adapter.mvadapter.utils.mesh_utils import (
+    Camera,
+    CameraProjection,
+    NVDiffRastContextWrapper,
+    SmartPainter,
+    TexturedMesh,
+    get_camera,
+    get_orthogonal_camera,
+    load_mesh,
+    render,
+    replace_mesh_texture_and_save,
+)
+from MV_Adapter.mvadapter.utils.mesh_utils.mesh_process import process_raw
+
+# Import dilate function to remove texture seams
+def torch_image_dilate(img):
+    """
+    Remove thin seams on generated texture
+        img (torch): [H, W, C]
+    """
+    import cv2
+    img = np.asarray(img.cpu().numpy(), dtype=np.float32)
+    img = img * 255
+    img = img.clip(0, 255)
+    mask = np.sum(img.astype(np.float32), axis=-1, keepdims=True)
+    mask = (mask <= 3.0).astype(np.float32)
+    kernel = np.ones((3, 3), 'uint8')
+    dilate_img = cv2.dilate(img, kernel, iterations=1)
+    img = img * (1 - mask) + dilate_img * mask
+    img = (img.clip(0, 255) / 255).astype(np.float32)
+    return torch.from_numpy(img)
+
+
+def clear():
+    torch.cuda.empty_cache()
+
+
+@contextmanager
+def mesh_use_texture(mesh: TexturedMesh, texture: torch.FloatTensor):
+    texture_ = mesh.texture
+    mesh.texture = texture
+    try:
+        yield
+    finally:
+        mesh.texture = texture_
+
+
+@dataclass
+class ModProcessConfig:
+    view_upscale: bool = False
+    view_upscale_factor: int = 2
+    inpaint_mode: str = "uv"  # in ["none", "uv", "view"]
+    view_inpaint_max_view_score_thresh: float = 0.02
+    view_inpaint_min_rounds: int = 4
+    view_inpaint_max_rounds: int = 8
+    view_inpaint_uv_padding_end: bool = True
+
+
+@dataclass
+class TexturePipelineOutput:
+    shaded_model_save_path: Optional[str] = None
+    pbr_model_save_path: Optional[str] = None
+
+
+class TexturePipeline:
+    def __init__(self, upscaler_ckpt_path: str, inpaint_ckpt_path: str, device: str):
+        self.device = device
+        self.ctx = NVDiffRastContextWrapper(device=self.device)
+        self.cam_proj = CameraProjection(
+            pb_backend="torch-cuda", bg_remover=None, device=self.device
+        )
+        if upscaler_ckpt_path is not None:
+            self.upscaler = ModelLoader().load_from_file(upscaler_ckpt_path)
+            self.upscaler.to(self.device).eval().half()
+        if inpaint_ckpt_path is not None:
+            self.inpainter = ModelLoader().load_from_file(inpaint_ckpt_path)
+            self.inpainter.to(self.device).eval()
+
+        self.smart_painter = SmartPainter(self.device)
+
+    def load_packed_images(self, packed_image_path: Optional[str]) -> List[Image.Image]:
+        if packed_image_path is None:
+            return None
+        packed_image = Image.open(packed_image_path)
+        images = np.array_split(np.array(packed_image), 6, axis=1)
+        images = [Image.fromarray(im) for im in images]
+        return images
+
+    def maybe_upscale_image(
+        self,
+        tensor: Optional[torch.FloatTensor],
+        upscale: bool,
+        upscale_factor: int,
+        batched: bool = False,
+    ) -> Optional[torch.FloatTensor]:
+        if upscale:
+            with torch.no_grad():
+                tensor = tensor.permute(0, 3, 1, 2)
+                if batched:
+                    tensor = self.upscaler(tensor.half()).float()
+                else:
+                    tensor = torch.concat(
+                        [
+                            self.upscaler(im.unsqueeze(0).half()).float()
+                            for im in tensor
+                        ],
+                        dim=0,
+                    )
+                tensor = tensor.clamp(0, 1).permute(0, 2, 3, 1)
+            clear()
+        return tensor
+
+    def view_inpaint(
+        self,
+        mod_name: str,
+        mesh: TexturedMesh,
+        uv_proj: torch.FloatTensor,
+        uv_valid_mask: torch.BoolTensor,
+        config: ModProcessConfig,
+        debug_dir: Optional[str] = None,
+    ) -> Tuple[torch.FloatTensor, torch.BoolTensor]:
+        def inpaint_func(
+            image: torch.FloatTensor, mask: torch.FloatTensor
+        ) -> torch.FloatTensor:
+            with torch.no_grad():
+                return self.inpainter(image.to(torch.float32), mask.to(torch.float32))
+
+        return self.smart_painter(
+            mod_name,
+            mesh,
+            inpaint_func,
+            uv_proj,
+            ~uv_valid_mask,
+            max_view_score_thresh=config.view_inpaint_max_view_score_thresh,
+            min_rounds=config.view_inpaint_min_rounds,
+            max_rounds=config.view_inpaint_max_rounds,
+            uv_padding_end=config.view_inpaint_uv_padding_end,
+            debug_dir=debug_dir,
+            debug_visualize_details=False,
+        )
+
+    def __call__(
+        self,
+        mesh_path: str,
+        save_dir: str,
+        save_name: str = "default",
+        # mesh load settings
+        move_to_center: bool = False,
+        front_x: bool = True,
+        # uv unwarp
+        uv_unwarp: bool = False,
+        preprocess_mesh: bool = False,
+        # projection
+        uv_size: int = 4096,
+        # modes
+        rgb_path: Optional[str] = None,
+        rgb_process_config: ModProcessConfig = ModProcessConfig(),
+        base_color_path: Optional[str] = None,
+        base_color_process_config: ModProcessConfig = ModProcessConfig(),
+        orm_path: Optional[str] = None,
+        orm_process_config: ModProcessConfig = ModProcessConfig(),
+        normal_path: Optional[str] = None,
+        normal_strength: float = 1.0,
+        normal_process_config: ModProcessConfig = ModProcessConfig(),
+        # inpaint
+        uv_inpaint_use_network: bool = False,
+        view_inpaint_include_occlusion_boundary: bool = False,
+        poisson_reprojection: bool = False,
+        # camera
+        camera_projection_type: str = "ORTHO",
+        camera_elevation_deg: List[float] = [0, 0, 0, 0, 89.99, -89.99],
+        camera_azimuth_deg: List[float] = [0, 90, 180, 270, 180, 180],
+        camera_distance: float = 1.0,
+        camera_ortho_scale: float = 1.1,
+        camera_fov_deg: float = 40,
+        # debug
+        debug_mode: bool = False,
+        # seam removal
+        apply_dilate: bool = True,
+    ):
+        clear()
+
+        if debug_mode:
+            debug_dir = os.path.join(save_dir, "debug")
+            os.makedirs(debug_dir, exist_ok=True)
+
+        if uv_unwarp:
+            file_suffix = os.path.splitext(mesh_path)[-1]
+            mesh_path_new = mesh_path.replace(file_suffix, f"_unwarp{file_suffix}")
+            process_raw(mesh_path, mesh_path_new, preprocess=preprocess_mesh)
+            mesh_path = mesh_path_new
+
+        mesh: TexturedMesh = load_mesh(
+            mesh_path,
+            rescale=True,
+            move_to_center=move_to_center,
+            front_x_to_y=front_x,
+            default_uv_size=uv_size,
+            device=self.device,
+        )
+
+        # projection
+        if camera_projection_type == "PERSP":
+            raise NotImplementedError
+        elif camera_projection_type == "ORTHO":
+            cameras = get_orthogonal_camera(
+                elevation_deg=camera_elevation_deg,
+                distance=[camera_distance] * 6,
+                left=-camera_ortho_scale / 2,
+                right=camera_ortho_scale / 2,
+                bottom=-camera_ortho_scale / 2,
+                top=camera_ortho_scale / 2,
+                azimuth_deg=[x - 90 for x in camera_azimuth_deg],  # -y as front
+                device=self.device,
+            )
+
+        mod_kwargs = {
+            "rgb": (rgb_path, rgb_process_config),
+            "base_color": (base_color_path, base_color_process_config),
+            "orm": (orm_path, orm_process_config),
+            "normal": (normal_path, normal_process_config),
+        }
+        mod_uv_image, mod_uv_tensor = {}, {}
+        for mod_name, (mod_path, mod_process_config) in mod_kwargs.items():
+            if mod_path is None:
+                mod_uv_image[mod_name] = None
+                mod_uv_tensor[mod_name] = None
+                continue
+            mod_images = self.load_packed_images(mod_path)
+            mod_tensor = image_to_tensor(mod_images, device=self.device)
+            mod_tensor = self.maybe_upscale_image(
+                mod_tensor,
+                mod_process_config.view_upscale,
+                mod_process_config.view_upscale_factor,
+            )
+            if mod_process_config.view_upscale and debug_mode:
+                make_image_grid(tensor_to_image(mod_tensor, batched=True), rows=1).save(
+                    os.path.join(debug_dir, f"{mod_name}_upscaled.jpg")
+                )
+
+            if mod_name == "normal":
+                _, height, width, _ = mod_tensor.shape
+                render_out = render(
+                    self.ctx,
+                    mesh,
+                    cameras,
+                    height=height,
+                    width=width,
+                    render_attr=False,
+                    render_depth=False,
+                    render_normal=True,
+                    render_tangent=True,
+                )
+
+                # compute UV tangent space
+                vN = render_out.normal
+                vT = render_out.tangent
+                vB = torch.cross(vN, vT, dim=-1)
+                tang_space = F.normalize(torch.stack([vT, vB, vN], dim=-2), dim=-1)
+
+                # compute geometry tangent space
+                vGN = vN
+                vGT = torch.as_tensor(
+                    [
+                        [1, 0, 0],
+                        [0, 1, 0],
+                        [-1, 0, 0],
+                        [0, -1, 0],
+                        [-1, 0, 0],
+                        [-1, 0, 0],
+                    ],
+                    dtype=torch.float32,
+                    device=self.device,
+                )[:, None, None, :]
+                vGB = torch.cross(vGN, vGT, dim=-1)
+                vGT = torch.cross(vGB, vGN, dim=-1)
+                geo_tang_space = F.normalize(
+                    torch.stack([vGT, vGB, vGN], dim=-2), dim=-1
+                )
+
+                # restore world-space normal from geometry tangent space
+                mod_tensor = mod_tensor * 2 - 1
+                mod_tensor = F.normalize(
+                    (
+                        mod_tensor[:, :, :, None, :]
+                        * geo_tang_space.permute(0, 1, 2, 4, 3)
+                    ).sum(-1),
+                    dim=-1,
+                )
+
+                # bake world-space normal to UV tangent space
+                mod_tensor = F.normalize(
+                    (mod_tensor[:, :, :, None, :] * tang_space).sum(-1), dim=-1
+                )
+                mod_tensor = (mod_tensor * 0.5 + 0.5).clamp(0, 1)
+
+                uv_proj, uv_valid_mask = self.cam_proj(
+                    mod_tensor,
+                    mesh,
+                    cameras,
+                    from_scratch=mod_process_config.inpaint_mode != "none",
+                    poisson_blending=False,
+                    depth_grad_dilation=5,
+                    uv_exp_blend_alpha=3,
+                    uv_exp_blend_view_weight=torch.as_tensor([1, 1, 1, 1, 1, 1]),
+                    aoi_cos_valid_threshold=0.2,
+                    uv_size=uv_size,
+                    return_uv_projection_mask=True,
+                )
+                uv_proj[~uv_valid_mask] = torch.as_tensor([0.5, 0.5, 1]).to(uv_proj)
+            else:
+                # TODO: tweak depth_grad_dilation
+                cam_proj_out = self.cam_proj(
+                    mod_tensor,
+                    mesh,
+                    cameras,
+                    from_scratch=mod_process_config.inpaint_mode != "none",
+                    poisson_blending=False,
+                    depth_grad_dilation=5,
+                    depth_grad_threshold=0.1,
+                    uv_exp_blend_alpha=3,
+                    uv_exp_blend_view_weight=torch.as_tensor([1, 1, 1, 1, 1, 1]),
+                    aoi_cos_valid_threshold=0.2,
+                    uv_size=uv_size,
+                    uv_padding=not uv_inpaint_use_network,
+                    return_dict=True,
+                )
+                uv_proj, uv_valid_mask, uv_depth_grad = (
+                    cam_proj_out.uv_proj,
+                    cam_proj_out.uv_proj_mask,
+                    cam_proj_out.uv_depth_grad,
+                )
+                if uv_inpaint_use_network:
+                    uv_inpaint_mask_input = 1 - uv_valid_mask[None, None].float()
+                    uv_inpaint_image_input = uv_proj[None].permute(0, 3, 1, 2)
+                    with torch.no_grad():
+                        uv_inpaint_result = self.inpainter(
+                            uv_inpaint_image_input, uv_inpaint_mask_input
+                        )[0].permute(1, 2, 0)
+                    clear()
+                    if debug_mode:
+                        make_image_grid(
+                            [
+                                tensor_to_image(uv_proj),
+                                tensor_to_image(uv_valid_mask),
+                                tensor_to_image(uv_inpaint_result),
+                            ]
+                        ).save(os.path.join(debug_dir, f"{mod_name}_uv_inpaint.jpg"))
+                    uv_proj = uv_inpaint_result.contiguous()
+
+                if mod_process_config.inpaint_mode == "view":
+                    if view_inpaint_include_occlusion_boundary:
+                        uv_max_depth_grad = uv_depth_grad.max(dim=0)[0]
+                        uv_valid_mask = uv_valid_mask & (uv_max_depth_grad < 0.1)
+                    uv_proj, uv_valid_mask = self.view_inpaint(
+                        mod_name,
+                        mesh,
+                        uv_proj,
+                        uv_valid_mask,
+                        mod_process_config,
+                        debug_dir=debug_dir if debug_mode else None,
+                    )
+
+                if poisson_reprojection:
+                    # up and down
+                    mesh.texture = uv_proj
+                    uv_proj = self.cam_proj(
+                        mod_tensor[4:5],
+                        mesh,
+                        cameras[4:5],
+                        from_scratch=False,
+                        poisson_blending=True,
+                        pb_keep_original_border=True,
+                        depth_grad_dilation=5,
+                        uv_exp_blend_alpha=3,
+                        uv_exp_blend_view_weight=torch.as_tensor([1, 1]),
+                        aoi_cos_valid_threshold=0.2,
+                        uv_size=uv_size,
+                        uv_padding=True,
+                        return_dict=False,
+                    )
+                    # front, sides and back
+                    mesh.texture = uv_proj
+                    uv_proj = self.cam_proj(
+                        mod_tensor[0:4],
+                        mesh,
+                        cameras[0:4],
+                        from_scratch=False,
+                        poisson_blending=True,
+                        pb_keep_original_border=True,
+                        depth_grad_dilation=5,
+                        uv_exp_blend_alpha=3,
+                        uv_exp_blend_view_weight=torch.as_tensor([1, 1, 1, 1]),
+                        aoi_cos_valid_threshold=0.2,
+                        uv_size=uv_size,
+                        uv_padding=True,
+                        return_dict=False,
+                    )
+
+                if mod_name == "orm":
+                    uv_proj[:, :, 0] = 1.0
+
+            # Apply dilate to remove texture seams before final conversion
+            if mod_name in ["rgb", "base_color"] and apply_dilate:  # Apply to color textures only
+                uv_proj = torch_image_dilate(uv_proj)
+
+            mod_uv_image[mod_name] = tensor_to_image(uv_proj)
+            mod_uv_tensor[mod_name] = uv_proj
+            clear()
+
+        shaded_model_save_path = None
+        if mod_uv_image["rgb"] is not None:
+            shaded_model_save_path = os.path.join(save_dir, f"{save_name}_shaded.glb")
+            replace_mesh_texture_and_save(
+                mesh_path,
+                shaded_model_save_path,
+                texture=mod_uv_image["rgb"],
+                backend="trimesh",
+                task_id=save_name,
+            )
+        pbr_model_save_path = None
+        if mod_uv_image["base_color"] is not None:
+            pbr_model_save_path = os.path.join(save_dir, f"{save_name}_pbr.glb")
+            replace_mesh_texture_and_save(
+                mesh_path,
+                pbr_model_save_path,
+                texture=mod_uv_image["base_color"],
+                metallic_roughness_texture=mod_uv_image["orm"],
+                normal_texture=mod_uv_image["normal"],
+                normal_strength=normal_strength,
+                backend="trimesh",
+                task_id=save_name,
+            )
+
+        clear()
+
+        return TexturePipelineOutput(
+            shaded_model_save_path=shaded_model_save_path,
+            pbr_model_save_path=pbr_model_save_path,
+        )
\ No newline at end of file
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/schedulers/scheduler_utils.py b/Gen_3D_Modules/MV_Adapter/mvadapter/schedulers/scheduler_utils.py
new file mode 100644
index 0000000..d25a55e
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/schedulers/scheduler_utils.py
@@ -0,0 +1,70 @@
+import torch
+
+
+def get_sigmas(noise_scheduler, timesteps, n_dim=4, dtype=torch.float32, device=None):
+    sigmas = noise_scheduler.sigmas.to(device=device, dtype=dtype)
+    schedule_timesteps = noise_scheduler.timesteps.to(device)
+    timesteps = timesteps.to(device)
+    step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+    sigma = sigmas[step_indices].flatten()
+    while len(sigma.shape) < n_dim:
+        sigma = sigma.unsqueeze(-1)
+    return sigma
+
+
+def SNR_to_betas(snr):
+    """
+    Converts SNR to betas
+    """
+    # alphas_cumprod = pass
+    # snr = (alpha / ) ** 2
+    # alpha_t^2 / (1 - alpha_t^2) = snr
+    alpha_t = (snr / (1 + snr)) ** 0.5
+    alphas_cumprod = alpha_t**2
+    alphas = alphas_cumprod / torch.cat(
+        [torch.ones(1, device=snr.device), alphas_cumprod[:-1]]
+    )
+    betas = 1 - alphas
+    return betas
+
+
+def compute_snr(timesteps, noise_scheduler):
+    """
+    Computes SNR as per Min-SNR-Diffusion-Training/guided_diffusion/gaussian_diffusion.py at 521b624bd70c67cee4bdf49225915f5
+    """
+    alphas_cumprod = noise_scheduler.alphas_cumprod
+    sqrt_alphas_cumprod = alphas_cumprod**0.5
+    sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+    # Expand the tensors.
+    # Adapted from Min-SNR-Diffusion-Training/guided_diffusion/gaussian_diffusion.py at 521b624bd70c67cee4bdf49225915f5
+    sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
+        timesteps
+    ].float()
+    while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+    alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+    sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(
+        device=timesteps.device
+    )[timesteps].float()
+    while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+    sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+    # Compute SNR.
+    snr = (alpha / sigma) ** 2
+    return snr
+
+
+def compute_alpha(timesteps, noise_scheduler):
+    alphas_cumprod = noise_scheduler.alphas_cumprod
+    sqrt_alphas_cumprod = alphas_cumprod**0.5
+    sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
+        timesteps
+    ].float()
+    while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+    alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+    return alpha
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/schedulers/scheduling_shift_snr.py b/Gen_3D_Modules/MV_Adapter/mvadapter/schedulers/scheduling_shift_snr.py
new file mode 100644
index 0000000..6da6150
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/schedulers/scheduling_shift_snr.py
@@ -0,0 +1,138 @@
+from typing import Any
+
+import torch
+
+from .scheduler_utils import SNR_to_betas, compute_snr
+
+
+class ShiftSNRScheduler:
+    def __init__(
+        self,
+        noise_scheduler: Any,
+        timesteps: Any,
+        shift_scale: float,
+        scheduler_class: Any,
+    ):
+        self.noise_scheduler = noise_scheduler
+        self.timesteps = timesteps
+        self.shift_scale = shift_scale
+        self.scheduler_class = scheduler_class
+
+    def _get_shift_scheduler(self):
+        """
+        Prepare scheduler for shifted betas.
+
+        :return: A scheduler object configured with shifted betas
+        """
+        snr = compute_snr(self.timesteps, self.noise_scheduler)
+        shifted_betas = SNR_to_betas(snr / self.shift_scale)
+
+        return self.scheduler_class.from_config(
+            self.noise_scheduler.config, trained_betas=shifted_betas.numpy()
+        )
+
+    def _get_interpolated_shift_scheduler(self):
+        """
+        Prepare scheduler for shifted betas and interpolate with the original betas in log space.
+
+        :return: A scheduler object configured with interpolated shifted betas
+        """
+        snr = compute_snr(self.timesteps, self.noise_scheduler)
+        shifted_snr = snr / self.shift_scale
+
+        weighting = self.timesteps.float() / (
+            self.noise_scheduler.config.num_train_timesteps - 1
+        )
+        interpolated_snr = torch.exp(
+            torch.log(snr) * (1 - weighting) + torch.log(shifted_snr) * weighting
+        )
+
+        shifted_betas = SNR_to_betas(interpolated_snr)
+
+        return self.scheduler_class.from_config(
+            self.noise_scheduler.config, trained_betas=shifted_betas.numpy()
+        )
+
+    @classmethod
+    def from_scheduler(
+        cls,
+        noise_scheduler: Any,
+        shift_mode: str = "default",
+        timesteps: Any = None,
+        shift_scale: float = 1.0,
+        scheduler_class: Any = None,
+    ):
+        # Check input
+        if timesteps is None:
+            timesteps = torch.arange(0, noise_scheduler.config.num_train_timesteps)
+        if scheduler_class is None:
+            scheduler_class = noise_scheduler.__class__
+
+        # Create scheduler
+        shift_scheduler = cls(
+            noise_scheduler=noise_scheduler,
+            timesteps=timesteps,
+            shift_scale=shift_scale,
+            scheduler_class=scheduler_class,
+        )
+
+        if shift_mode == "default":
+            return shift_scheduler._get_shift_scheduler()
+        elif shift_mode == "interpolated":
+            return shift_scheduler._get_interpolated_shift_scheduler()
+        else:
+            raise ValueError(f"Unknown shift_mode: {shift_mode}")
+
+
+if __name__ == "__main__":
+    """
+    Compare the alpha values for different noise schedulers.
+    """
+    import matplotlib.pyplot as plt
+    from diffusers import DDPMScheduler
+
+    from .scheduler_utils import compute_alpha
+
+    # Base
+    timesteps = torch.arange(0, 1000)
+    noise_scheduler_base = DDPMScheduler.from_pretrained(
+        "runwayml/stable-diffusion-v1-5", subfolder="scheduler"
+    )
+    alpha = compute_alpha(timesteps, noise_scheduler_base)
+    plt.plot(timesteps.numpy(), alpha.numpy(), label="Base")
+
+    # Kolors
+    num_train_timesteps_ = 1100
+    timesteps_ = torch.arange(0, num_train_timesteps_)
+    noise_kwargs = {"beta_end": 0.014, "num_train_timesteps": num_train_timesteps_}
+    noise_scheduler_kolors = DDPMScheduler.from_config(
+        noise_scheduler_base.config, **noise_kwargs
+    )
+    alpha = compute_alpha(timesteps_, noise_scheduler_kolors)
+    plt.plot(timesteps_.numpy(), alpha.numpy(), label="Kolors")
+
+    # Shift betas
+    shift_scale = 8.0
+    noise_scheduler_shift = ShiftSNRScheduler.from_scheduler(
+        noise_scheduler_base, shift_mode="default", shift_scale=shift_scale
+    )
+    alpha = compute_alpha(timesteps, noise_scheduler_shift)
+    plt.plot(timesteps.numpy(), alpha.numpy(), label="Shift Noise (scale 8.0)")
+
+    # Shift betas (interpolated)
+    noise_scheduler_inter = ShiftSNRScheduler.from_scheduler(
+        noise_scheduler_base, shift_mode="interpolated", shift_scale=shift_scale
+    )
+    alpha = compute_alpha(timesteps, noise_scheduler_inter)
+    plt.plot(timesteps.numpy(), alpha.numpy(), label="Interpolated (scale 8.0)")
+
+    # ZeroSNR
+    noise_scheduler = DDPMScheduler.from_config(
+        noise_scheduler_base.config, rescale_betas_zero_snr=True
+    )
+    alpha = compute_alpha(timesteps, noise_scheduler)
+    plt.plot(timesteps.numpy(), alpha.numpy(), label="ZeroSNR")
+
+    plt.legend()
+    plt.grid()
+    plt.savefig("check_alpha.png")
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/__init__.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/__init__.py
new file mode 100644
index 0000000..8e4b96f
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/__init__.py
@@ -0,0 +1,3 @@
+from .mesh_utils.camera import get_camera, get_orthogonal_camera
+from .geometry import get_plucker_embeds_from_cameras_ortho
+from .saving import make_image_grid, tensor_to_image, image_to_tensor
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/base.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/base.py
new file mode 100644
index 0000000..f78adda
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/base.py
@@ -0,0 +1,123 @@
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+
+from .config import parse_structured
+from .misc import get_device, load_module_weights
+from .typing import *
+
+
+class Configurable:
+    @dataclass
+    class Config:
+        pass
+
+    def __init__(self, cfg: Optional[dict] = None) -> None:
+        super().__init__()
+        self.cfg = parse_structured(self.Config, cfg)
+
+
+class Updateable:
+    def do_update_step(
+        self, epoch: int, global_step: int, on_load_weights: bool = False
+    ):
+        for attr in self.__dir__():
+            if attr.startswith("_"):
+                continue
+            try:
+                module = getattr(self, attr)
+            except:
+                continue  # ignore attributes like property, which can't be retrived using getattr?
+            if isinstance(module, Updateable):
+                module.do_update_step(
+                    epoch, global_step, on_load_weights=on_load_weights
+                )
+        self.update_step(epoch, global_step, on_load_weights=on_load_weights)
+
+    def do_update_step_end(self, epoch: int, global_step: int):
+        for attr in self.__dir__():
+            if attr.startswith("_"):
+                continue
+            try:
+                module = getattr(self, attr)
+            except:
+                continue  # ignore attributes like property, which can't be retrived using getattr?
+            if isinstance(module, Updateable):
+                module.do_update_step_end(epoch, global_step)
+        self.update_step_end(epoch, global_step)
+
+    def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
+        # override this method to implement custom update logic
+        # if on_load_weights is True, you should be careful doing things related to model evaluations,
+        # as the models and tensors are not guarenteed to be on the same device
+        pass
+
+    def update_step_end(self, epoch: int, global_step: int):
+        pass
+
+
+def update_if_possible(module: Any, epoch: int, global_step: int) -> None:
+    if isinstance(module, Updateable):
+        module.do_update_step(epoch, global_step)
+
+
+def update_end_if_possible(module: Any, epoch: int, global_step: int) -> None:
+    if isinstance(module, Updateable):
+        module.do_update_step_end(epoch, global_step)
+
+
+class BaseObject(Updateable):
+    @dataclass
+    class Config:
+        pass
+
+    cfg: Config  # add this to every subclass of BaseObject to enable static type checking
+
+    def __init__(
+        self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs
+    ) -> None:
+        super().__init__()
+        self.cfg = parse_structured(self.Config, cfg)
+        self.device = get_device()
+        self.configure(*args, **kwargs)
+
+    def configure(self, *args, **kwargs) -> None:
+        pass
+
+
+class BaseModule(nn.Module, Updateable):
+    @dataclass
+    class Config:
+        weights: Optional[str] = None
+
+    cfg: Config  # add this to every subclass of BaseModule to enable static type checking
+
+    def __init__(
+        self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs
+    ) -> None:
+        super().__init__()
+        self.cfg = parse_structured(self.Config, cfg)
+        self.device = get_device()
+        self._non_modules = {}
+        self.configure(*args, **kwargs)
+        if self.cfg.weights is not None:
+            # format: path/to/weights:module_name
+            weights_path, module_name = self.cfg.weights.split(":")
+            state_dict, epoch, global_step = load_module_weights(
+                weights_path, module_name=module_name, map_location="cpu"
+            )
+            self.load_state_dict(state_dict)
+            self.do_update_step(
+                epoch, global_step, on_load_weights=True
+            )  # restore states
+
+    def configure(self, *args, **kwargs) -> None:
+        pass
+
+    def register_non_module(self, name: str, module: nn.Module) -> None:
+        # non-modules won't be treated as model parameters
+        self._non_modules[name] = module
+
+    def non_module(self, name: str):
+        return self._non_modules.get(name, None)
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/callbacks.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/callbacks.py
new file mode 100644
index 0000000..50cb08a
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/callbacks.py
@@ -0,0 +1,158 @@
+import os
+import shutil
+import subprocess
+
+import pytorch_lightning
+
+from .config import dump_config
+from .misc import parse_version
+
+if parse_version(pytorch_lightning.__version__) > parse_version("1.8"):
+    from pytorch_lightning.callbacks import Callback
+else:
+    from pytorch_lightning.callbacks.base import Callback
+
+from pytorch_lightning.callbacks.progress import TQDMProgressBar
+from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
+
+
+class VersionedCallback(Callback):
+    def __init__(self, save_root, version=None, use_version=True):
+        self.save_root = save_root
+        self._version = version
+        self.use_version = use_version
+
+    @property
+    def version(self) -> int:
+        """Get the experiment version.
+
+        Returns:
+            The experiment version if specified else the next version.
+        """
+        if self._version is None:
+            self._version = self._get_next_version()
+        return self._version
+
+    def _get_next_version(self):
+        existing_versions = []
+        if os.path.isdir(self.save_root):
+            for f in os.listdir(self.save_root):
+                bn = os.path.basename(f)
+                if bn.startswith("version_"):
+                    dir_ver = os.path.splitext(bn)[0].split("_")[1].replace("/", "")
+                    existing_versions.append(int(dir_ver))
+        if len(existing_versions) == 0:
+            return 0
+        return max(existing_versions) + 1
+
+    @property
+    def savedir(self):
+        if not self.use_version:
+            return self.save_root
+        return os.path.join(
+            self.save_root,
+            (
+                self.version
+                if isinstance(self.version, str)
+                else f"version_{self.version}"
+            ),
+        )
+
+
+class CodeSnapshotCallback(VersionedCallback):
+    def __init__(self, save_root, version=None, use_version=True):
+        super().__init__(save_root, version, use_version)
+
+    def get_file_list(self):
+        return [
+            b.decode()
+            for b in set(
+                subprocess.check_output(
+                    'git ls-files -- ":!:load/*"', shell=True
+                ).splitlines()
+            )
+            | set(  # hard code, TODO: use config to exclude folders or files
+                subprocess.check_output(
+                    "git ls-files --others --exclude-standard", shell=True
+                ).splitlines()
+            )
+        ]
+
+    @rank_zero_only
+    def save_code_snapshot(self):
+        os.makedirs(self.savedir, exist_ok=True)
+        for f in self.get_file_list():
+            if not os.path.exists(f) or os.path.isdir(f):
+                continue
+            os.makedirs(os.path.join(self.savedir, os.path.dirname(f)), exist_ok=True)
+            shutil.copyfile(f, os.path.join(self.savedir, f))
+
+    def on_fit_start(self, trainer, pl_module):
+        try:
+            self.save_code_snapshot()
+        except:
+            rank_zero_warn(
+                "Code snapshot is not saved. Please make sure you have git installed and are in a git repository."
+            )
+
+
+class ConfigSnapshotCallback(VersionedCallback):
+    def __init__(self, config_path, config, save_root, version=None, use_version=True):
+        super().__init__(save_root, version, use_version)
+        self.config_path = config_path
+        self.config = config
+
+    @rank_zero_only
+    def save_config_snapshot(self):
+        os.makedirs(self.savedir, exist_ok=True)
+        dump_config(os.path.join(self.savedir, "parsed.yaml"), self.config)
+        shutil.copyfile(self.config_path, os.path.join(self.savedir, "raw.yaml"))
+
+    def on_fit_start(self, trainer, pl_module):
+        self.save_config_snapshot()
+
+
+class CustomProgressBar(TQDMProgressBar):
+    def get_metrics(self, *args, **kwargs):
+        # don't show the version number
+        items = super().get_metrics(*args, **kwargs)
+        items.pop("v_num", None)
+        return items
+
+
+class ProgressCallback(Callback):
+    def __init__(self, save_path):
+        super().__init__()
+        self.save_path = save_path
+        self._file_handle = None
+
+    @property
+    def file_handle(self):
+        if self._file_handle is None:
+            self._file_handle = open(self.save_path, "w")
+        return self._file_handle
+
+    @rank_zero_only
+    def write(self, msg: str) -> None:
+        self.file_handle.seek(0)
+        self.file_handle.truncate()
+        self.file_handle.write(msg)
+        self.file_handle.flush()
+
+    @rank_zero_only
+    def on_train_batch_end(self, trainer, pl_module, *args, **kwargs):
+        self.write(
+            f"Generation progress: {pl_module.true_global_step / trainer.max_steps * 100:.2f}%"
+        )
+
+    @rank_zero_only
+    def on_validation_start(self, trainer, pl_module):
+        self.write(f"Rendering validation image ...")
+
+    @rank_zero_only
+    def on_test_start(self, trainer, pl_module):
+        self.write(f"Rendering video ...")
+
+    @rank_zero_only
+    def on_predict_start(self, trainer, pl_module):
+        self.write(f"Exporting mesh assets ...")
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/config.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/config.py
new file mode 100644
index 0000000..1768835
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/config.py
@@ -0,0 +1,140 @@
+import os
+from dataclasses import dataclass, field
+from datetime import datetime
+
+from omegaconf import OmegaConf
+
+from .core import debug, find, info, warn
+from .typing import *
+
+# ============ Register OmegaConf Resolvers ============= #
+OmegaConf.register_new_resolver(
+    "calc_exp_lr_decay_rate", lambda factor, n: factor ** (1.0 / n)
+)
+OmegaConf.register_new_resolver("add", lambda a, b: a + b)
+OmegaConf.register_new_resolver("sub", lambda a, b: a - b)
+OmegaConf.register_new_resolver("mul", lambda a, b: a * b)
+OmegaConf.register_new_resolver("div", lambda a, b: a / b)
+OmegaConf.register_new_resolver("idiv", lambda a, b: a // b)
+OmegaConf.register_new_resolver("basename", lambda p: os.path.basename(p))
+OmegaConf.register_new_resolver("rmspace", lambda s, sub: s.replace(" ", sub))
+OmegaConf.register_new_resolver("tuple2", lambda s: [float(s), float(s)])
+OmegaConf.register_new_resolver("gt0", lambda s: s > 0)
+OmegaConf.register_new_resolver("not", lambda s: not s)
+
+
+def calc_num_train_steps(num_data, batch_size, max_epochs, num_nodes, num_cards=8):
+    return int(num_data / (num_nodes * num_cards * batch_size)) * max_epochs
+
+
+OmegaConf.register_new_resolver("calc_num_train_steps", calc_num_train_steps)
+
+# ======================================================= #
+
+
+# ============== Automatic Name Resolvers =============== #
+def get_naming_convention(cfg):
+    # TODO
+    name = f"lrm_{cfg.system.backbone.num_layers}"
+    return name
+
+
+# ======================================================= #
+
+
+@dataclass
+class ExperimentConfig:
+    name: str = "default"
+    description: str = ""
+    tag: str = ""
+    seed: int = 0
+    use_timestamp: bool = True
+    timestamp: Optional[str] = None
+    exp_root_dir: str = "outputs"
+
+    ### these shouldn't be set manually
+    exp_dir: str = "outputs/default"
+    trial_name: str = "exp"
+    trial_dir: str = "outputs/default/exp"
+    n_gpus: int = 1
+    ###
+
+    resume: Optional[str] = None
+
+    data_cls: str = ""
+    data: dict = field(default_factory=dict)
+
+    system_cls: str = ""
+    system: dict = field(default_factory=dict)
+
+    # accept pytorch-lightning trainer parameters
+    # see https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api
+    trainer: dict = field(default_factory=dict)
+
+    # accept pytorch-lightning checkpoint callback parameters
+    # see https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html#modelcheckpoint
+    checkpoint: dict = field(default_factory=dict)
+
+
+def load_config(
+    *yamls: str, cli_args: list = [], from_string=False, makedirs=True, **kwargs
+) -> Any:
+    if from_string:
+        parse_func = OmegaConf.create
+    else:
+        parse_func = OmegaConf.load
+    yaml_confs = []
+    for y in yamls:
+        conf = parse_func(y)
+        extends = conf.pop("extends", None)
+        if extends:
+            assert os.path.exists(extends), f"File {extends} does not exist."
+            yaml_confs.append(OmegaConf.load(extends))
+        yaml_confs.append(conf)
+    cli_conf = OmegaConf.from_cli(cli_args)
+    cfg = OmegaConf.merge(*yaml_confs, cli_conf, kwargs)
+    OmegaConf.resolve(cfg)
+    assert isinstance(cfg, DictConfig)
+    scfg: ExperimentConfig = parse_structured(ExperimentConfig, cfg)
+
+    # post processing
+    # auto naming
+    if scfg.name == "auto":
+        scfg.name = get_naming_convention(scfg)
+    # add timestamp
+    if not scfg.tag and not scfg.use_timestamp:
+        raise ValueError("Either tag is specified or use_timestamp is True.")
+    scfg.trial_name = scfg.tag
+    # if resume from an existing config, scfg.timestamp should not be None
+    if scfg.timestamp is None:
+        scfg.timestamp = ""
+        if scfg.use_timestamp:
+            if scfg.n_gpus > 1:
+                warn(
+                    "Timestamp is disabled when using multiple GPUs, please make sure you have a unique tag."
+                )
+            else:
+                scfg.timestamp = datetime.now().strftime("@%Y%m%d-%H%M%S")
+    # make directories
+    scfg.trial_name += scfg.timestamp
+    scfg.exp_dir = os.path.join(scfg.exp_root_dir, scfg.name)
+    scfg.trial_dir = os.path.join(scfg.exp_dir, scfg.trial_name)
+
+    if makedirs:
+        os.makedirs(scfg.trial_dir, exist_ok=True)
+
+    return scfg
+
+
+def config_to_primitive(config, resolve: bool = True) -> Any:
+    return OmegaConf.to_container(config, resolve=resolve)
+
+
+def dump_config(path: str, config) -> None:
+    with open(path, "w") as fp:
+        OmegaConf.save(config=config, f=fp)
+
+
+def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any:
+    scfg = OmegaConf.merge(OmegaConf.structured(fields), cfg)
+    return scfg
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/core.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/core.py
new file mode 100644
index 0000000..7c26c6d
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/core.py
@@ -0,0 +1,29 @@
+import importlib
+
+###  grammar sugar for logging utilities  ###
+import logging
+
+logger = logging.getLogger("pytorch_lightning")
+
+from pytorch_lightning.utilities.rank_zero import (
+    rank_zero_debug,
+    rank_zero_info,
+    rank_zero_only,
+)
+
+
+def find(cls_string):
+    module_string = ".".join(cls_string.split(".")[:-1])
+    cls_name = cls_string.split(".")[-1]
+    module = importlib.import_module(module_string, package=None)
+    cls = getattr(module, cls_name)
+    return cls
+
+
+debug = rank_zero_debug
+info = rank_zero_info
+
+
+@rank_zero_only
+def warn(*args, **kwargs):
+    logger.warn(*args, **kwargs)
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/geometry.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/geometry.py
new file mode 100644
index 0000000..3ae9196
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/geometry.py
@@ -0,0 +1,253 @@
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+
+def get_position_map_from_depth(depth, mask, intrinsics, extrinsics, image_wh=None):
+    """Compute the position map from the depth map and the camera parameters for a batch of views.
+
+    Args:
+        depth (torch.Tensor): The depth maps with the shape (B, H, W, 1).
+        mask (torch.Tensor): The masks with the shape (B, H, W, 1).
+        intrinsics (torch.Tensor): The camera intrinsics matrices with the shape (B, 3, 3).
+        extrinsics (torch.Tensor): The camera extrinsics matrices with the shape (B, 4, 4).
+        image_wh (Tuple[int, int]): The image width and height.
+
+    Returns:
+        torch.Tensor: The position maps with the shape (B, H, W, 3).
+    """
+    if image_wh is None:
+        image_wh = depth.shape[2], depth.shape[1]
+
+    B, H, W, _ = depth.shape
+    depth = depth.squeeze(-1)
+
+    u_coord, v_coord = torch.meshgrid(
+        torch.arange(image_wh[0]), torch.arange(image_wh[1]), indexing="xy"
+    )
+    u_coord = u_coord.type_as(depth).unsqueeze(0).expand(B, -1, -1)
+    v_coord = v_coord.type_as(depth).unsqueeze(0).expand(B, -1, -1)
+
+    # Compute the position map by back-projecting depth pixels to 3D space
+    x = (
+        (u_coord - intrinsics[:, 0, 2].unsqueeze(-1).unsqueeze(-1))
+        * depth
+        / intrinsics[:, 0, 0].unsqueeze(-1).unsqueeze(-1)
+    )
+    y = (
+        (v_coord - intrinsics[:, 1, 2].unsqueeze(-1).unsqueeze(-1))
+        * depth
+        / intrinsics[:, 1, 1].unsqueeze(-1).unsqueeze(-1)
+    )
+    z = depth
+
+    # Concatenate to form the 3D coordinates in the camera frame
+    camera_coords = torch.stack([x, y, z], dim=-1)
+
+    # Apply the extrinsic matrix to get coordinates in the world frame
+    coords_homogeneous = torch.nn.functional.pad(
+        camera_coords, (0, 1), "constant", 1.0
+    )  # Add a homogeneous coordinate
+    world_coords = torch.matmul(
+        coords_homogeneous.view(B, -1, 4), extrinsics.transpose(1, 2)
+    ).view(B, H, W, 4)
+
+    # Apply the mask to the position map
+    position_map = world_coords[..., :3] * mask
+
+    return position_map
+
+
+def get_position_map_from_depth_ortho(
+    depth, mask, extrinsics, ortho_scale, image_wh=None
+):
+    """Compute the position map from the depth map and the camera parameters for a batch of views
+    using orthographic projection with a given ortho_scale.
+
+    Args:
+        depth (torch.Tensor): The depth maps with the shape (B, H, W, 1).
+        mask (torch.Tensor): The masks with the shape (B, H, W, 1).
+        extrinsics (torch.Tensor): The camera extrinsics matrices with the shape (B, 4, 4).
+        ortho_scale (torch.Tensor): The scaling factor for the orthographic projection with the shape (B, 1, 1, 1).
+        image_wh (Tuple[int, int]): Optional. The image width and height.
+
+    Returns:
+        torch.Tensor: The position maps with the shape (B, H, W, 3).
+    """
+    if image_wh is None:
+        image_wh = depth.shape[2], depth.shape[1]
+
+    B, H, W, _ = depth.shape
+    depth = depth.squeeze(-1)
+
+    # Generating grid of coordinates in the image space
+    u_coord, v_coord = torch.meshgrid(
+        torch.arange(0, image_wh[0]), torch.arange(0, image_wh[1]), indexing="xy"
+    )
+    u_coord = u_coord.type_as(depth).unsqueeze(0).expand(B, -1, -1)
+    v_coord = v_coord.type_as(depth).unsqueeze(0).expand(B, -1, -1)
+
+    # Compute the position map using orthographic projection with ortho_scale
+    x = (u_coord - image_wh[0] / 2) * ortho_scale / image_wh[0]
+    y = (v_coord - image_wh[1] / 2) * ortho_scale / image_wh[1]
+    z = depth
+
+    # Concatenate to form the 3D coordinates in the camera frame
+    camera_coords = torch.stack([x, y, z], dim=-1)
+
+    # Apply the extrinsic matrix to get coordinates in the world frame
+    coords_homogeneous = torch.nn.functional.pad(
+        camera_coords, (0, 1), "constant", 1.0
+    )  # Add a homogeneous coordinate
+    world_coords = torch.matmul(
+        coords_homogeneous.view(B, -1, 4), extrinsics.transpose(1, 2)
+    ).view(B, H, W, 4)
+
+    # Apply the mask to the position map
+    position_map = world_coords[..., :3] * mask
+
+    return position_map
+
+
+def get_opencv_from_blender(matrix_world, fov=None, image_size=None):
+    # convert matrix_world to opencv format extrinsics
+    opencv_world_to_cam = matrix_world.inverse()
+    opencv_world_to_cam[1, :] *= -1
+    opencv_world_to_cam[2, :] *= -1
+    R, T = opencv_world_to_cam[:3, :3], opencv_world_to_cam[:3, 3]
+
+    if fov is None:  # orthographic camera
+        return R, T
+
+    R, T = R.unsqueeze(0), T.unsqueeze(0)
+    # convert fov to opencv format intrinsics
+    focal = 1 / np.tan(fov / 2)
+    intrinsics = np.diag(np.array([focal, focal, 1])).astype(np.float32)
+    opencv_cam_matrix = (
+        torch.from_numpy(intrinsics).unsqueeze(0).float().to(matrix_world.device)
+    )
+    opencv_cam_matrix[:, :2, -1] += torch.tensor([image_size / 2, image_size / 2]).to(
+        matrix_world.device
+    )
+    opencv_cam_matrix[:, [0, 1], [0, 1]] *= image_size / 2
+
+    return R, T, opencv_cam_matrix
+
+
+def get_ray_directions(
+    H: int,
+    W: int,
+    focal: float,
+    principal: Optional[Tuple[float, float]] = None,
+    use_pixel_centers: bool = True,
+) -> torch.Tensor:
+    """
+    Get ray directions for all pixels in camera coordinate.
+    Args:
+        H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers
+    Outputs:
+        directions: (H, W, 3), the direction of the rays in camera coordinate
+    """
+    pixel_center = 0.5 if use_pixel_centers else 0
+    cx, cy = W / 2, H / 2 if principal is None else principal
+    i, j = torch.meshgrid(
+        torch.arange(W, dtype=torch.float32) + pixel_center,
+        torch.arange(H, dtype=torch.float32) + pixel_center,
+        indexing="xy",
+    )
+    directions = torch.stack(
+        [(i - cx) / focal, -(j - cy) / focal, -torch.ones_like(i)], -1
+    )
+    return F.normalize(directions, dim=-1)
+
+
+def get_rays(
+    directions: torch.Tensor, c2w: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Get ray origins and directions from camera coordinates to world coordinates
+    Args:
+        directions: (H, W, 3) ray directions in camera coordinates
+        c2w: (4, 4) camera-to-world transformation matrix
+    Outputs:
+        rays_o, rays_d: (H, W, 3) ray origins and directions in world coordinates
+    """
+    # Rotate ray directions from camera coordinate to the world coordinate
+    rays_d = directions @ c2w[:3, :3].T
+    rays_o = c2w[:3, 3].expand(rays_d.shape)
+    return rays_o, rays_d
+
+
+def compute_plucker_embed(
+    c2w: torch.Tensor, image_width: int, image_height: int, focal: float
+) -> torch.Tensor:
+    """
+    Computes Plucker coordinates for a camera.
+    Args:
+        c2w: (4, 4) camera-to-world transformation matrix
+        image_width: Image width
+        image_height: Image height
+        focal: Focal length of the camera
+    Returns:
+        plucker: (6, H, W) Plucker embedding
+    """
+    directions = get_ray_directions(image_height, image_width, focal)
+    rays_o, rays_d = get_rays(directions, c2w)
+    # Cross product to get Plucker coordinates
+    cross = torch.cross(rays_o, rays_d, dim=-1)
+    plucker = torch.cat((rays_d, cross), dim=-1)
+    return plucker.permute(2, 0, 1)
+
+
+def get_plucker_embeds_from_cameras(
+    c2w: List[torch.Tensor], fov: List[float], image_size: int
+) -> torch.Tensor:
+    """
+    Given lists of camera transformations and fov, returns the batched plucker embeddings.
+    Args:
+        c2w: list of camera-to-world transformation matrices
+        fov: list of field of view values
+        image_size: size of the image
+    Returns:
+        plucker_embeds: (B, 6, H, W) batched plucker embeddings
+    """
+    plucker_embeds = []
+    for cam_matrix, cam_fov in zip(c2w, fov):
+        focal = 0.5 * image_size / np.tan(0.5 * cam_fov)
+        plucker = compute_plucker_embed(cam_matrix, image_size, image_size, focal)
+        plucker_embeds.append(plucker)
+    return torch.stack(plucker_embeds)
+
+
+def get_plucker_embeds_from_cameras_ortho(
+    c2w: List[torch.Tensor], ortho_scale: List[float], image_size: int
+):
+    """
+    Given lists of camera transformations and fov, returns the batched plucker embeddings.
+
+    Parameters:
+        c2w: list of camera-to-world transformation matrices
+        fov: list of field of view values
+        image_size: size of the image
+
+    Returns:
+        plucker_embeds: plucker embeddings (B, 6, H, W)
+    """
+    plucker_embeds = []
+    # compute pairwise mask and plucker embeddings
+    for cam_matrix, scale in zip(c2w, ortho_scale):
+        # blender to opencv to pytorch3d
+        R, T = get_opencv_from_blender(cam_matrix)
+        cam_pos = -R.T @ T
+        view_dir = R.T @ torch.tensor([0, 0, 1]).float().to(cam_matrix.device)
+        # normalize camera position
+        cam_pos = F.normalize(cam_pos, dim=0)
+        plucker = torch.concat([view_dir, cam_pos])
+        plucker = plucker.unsqueeze(-1).unsqueeze(-1).repeat(1, image_size, image_size)
+        plucker_embeds.append(plucker)
+
+    plucker_embeds = torch.stack(plucker_embeds)
+
+    return plucker_embeds
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/logging.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/logging.py
new file mode 100644
index 0000000..240dcbc
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/logging.py
@@ -0,0 +1,340 @@
+# coding=utf-8
+# Copyright 2024 Optuna, Hugging Face
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Logging utilities."""
+
+import logging
+import os
+import sys
+import threading
+from logging import CRITICAL  # NOQA
+from logging import DEBUG  # NOQA
+from logging import ERROR  # NOQA
+from logging import FATAL  # NOQA
+from logging import INFO  # NOQA
+from logging import NOTSET  # NOQA
+from logging import WARN  # NOQA
+from logging import WARNING  # NOQA
+from typing import Dict, Optional
+
+from tqdm import auto as tqdm_lib
+
+_lock = threading.Lock()
+_default_handler: Optional[logging.Handler] = None
+
+log_levels = {
+    "debug": logging.DEBUG,
+    "info": logging.INFO,
+    "warning": logging.WARNING,
+    "error": logging.ERROR,
+    "critical": logging.CRITICAL,
+}
+
+_default_log_level = logging.INFO
+
+_tqdm_active = True
+
+
+def _get_default_logging_level() -> int:
+    """
+    If LATEXTURE_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
+    not - fall back to `_default_log_level`
+    """
+    env_level_str = os.getenv("LATEXTURE_VERBOSITY", None)
+    if env_level_str:
+        if env_level_str in log_levels:
+            return log_levels[env_level_str]
+        else:
+            logging.getLogger().warning(
+                f"Unknown option LATEXTURE_VERBOSITY={env_level_str}, "
+                f"has to be one of: { ', '.join(log_levels.keys()) }"
+            )
+    return _default_log_level
+
+
+def _get_library_name() -> str:
+    return __name__.split(".")[0]
+
+
+def _get_library_root_logger() -> logging.Logger:
+    return logging.getLogger(_get_library_name())
+
+
+def _configure_library_root_logger() -> None:
+    global _default_handler
+
+    with _lock:
+        if _default_handler:
+            # This library has already configured the library root logger.
+            return
+        _default_handler = logging.StreamHandler()  # Set sys.stderr as stream.
+        _default_handler.flush = sys.stderr.flush
+
+        # Apply our default configuration to the library root logger.
+        library_root_logger = _get_library_root_logger()
+        library_root_logger.addHandler(_default_handler)
+        library_root_logger.setLevel(_get_default_logging_level())
+        library_root_logger.propagate = False
+
+        enable_explicit_format()
+
+
+def _reset_library_root_logger() -> None:
+    global _default_handler
+
+    with _lock:
+        if not _default_handler:
+            return
+
+        library_root_logger = _get_library_root_logger()
+        library_root_logger.removeHandler(_default_handler)
+        library_root_logger.setLevel(logging.NOTSET)
+        _default_handler = None
+
+
+def get_log_levels_dict() -> Dict[str, int]:
+    return log_levels
+
+
+def get_logger(name: Optional[str] = None) -> logging.Logger:
+    """
+    Return a logger with the specified name.
+
+    This function is not supposed to be directly accessed unless you are writing a custom diffusers module.
+    """
+
+    if name is None:
+        name = _get_library_name()
+
+    _configure_library_root_logger()
+    return logging.getLogger(name)
+
+
+def get_verbosity() -> int:
+    """
+    Return the current level for the 🤗 Diffusers' root logger as an `int`.
+
+    Returns:
+        `int`:
+            Logging level integers which can be one of:
+
+            - `50`: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
+            - `40`: `diffusers.logging.ERROR`
+            - `30`: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
+            - `20`: `diffusers.logging.INFO`
+            - `10`: `diffusers.logging.DEBUG`
+
+    """
+
+    _configure_library_root_logger()
+    return _get_library_root_logger().getEffectiveLevel()
+
+
+def set_verbosity(verbosity: int) -> None:
+    """
+    Set the verbosity level for the 🤗 Diffusers' root logger.
+
+    Args:
+        verbosity (`int`):
+            Logging level which can be one of:
+
+            - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
+            - `diffusers.logging.ERROR`
+            - `diffusers.logging.WARNING` or `diffusers.logging.WARN`
+            - `diffusers.logging.INFO`
+            - `diffusers.logging.DEBUG`
+    """
+
+    _configure_library_root_logger()
+    _get_library_root_logger().setLevel(verbosity)
+
+
+def set_verbosity_info() -> None:
+    """Set the verbosity to the `INFO` level."""
+    return set_verbosity(INFO)
+
+
+def set_verbosity_warning() -> None:
+    """Set the verbosity to the `WARNING` level."""
+    return set_verbosity(WARNING)
+
+
+def set_verbosity_debug() -> None:
+    """Set the verbosity to the `DEBUG` level."""
+    return set_verbosity(DEBUG)
+
+
+def set_verbosity_error() -> None:
+    """Set the verbosity to the `ERROR` level."""
+    return set_verbosity(ERROR)
+
+
+def disable_default_handler() -> None:
+    """Disable the default handler of the 🤗 Diffusers' root logger."""
+
+    _configure_library_root_logger()
+
+    assert _default_handler is not None
+    _get_library_root_logger().removeHandler(_default_handler)
+
+
+def enable_default_handler() -> None:
+    """Enable the default handler of the 🤗 Diffusers' root logger."""
+
+    _configure_library_root_logger()
+
+    assert _default_handler is not None
+    _get_library_root_logger().addHandler(_default_handler)
+
+
+def add_handler(handler: logging.Handler) -> None:
+    """adds a handler to the HuggingFace Diffusers' root logger."""
+
+    _configure_library_root_logger()
+
+    assert handler is not None
+    _get_library_root_logger().addHandler(handler)
+
+
+def remove_handler(handler: logging.Handler) -> None:
+    """removes given handler from the HuggingFace Diffusers' root logger."""
+
+    _configure_library_root_logger()
+
+    assert handler is not None and handler in _get_library_root_logger().handlers
+    _get_library_root_logger().removeHandler(handler)
+
+
+def disable_propagation() -> None:
+    """
+    Disable propagation of the library log outputs. Note that log propagation is disabled by default.
+    """
+
+    _configure_library_root_logger()
+    _get_library_root_logger().propagate = False
+
+
+def enable_propagation() -> None:
+    """
+    Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent
+    double logging if the root logger has been configured.
+    """
+
+    _configure_library_root_logger()
+    _get_library_root_logger().propagate = True
+
+
+def enable_explicit_format() -> None:
+    """
+    Enable explicit formatting for every 🤗 Diffusers' logger. The explicit formatter is as follows:
+    ```
+    [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
+    ```
+    All handlers currently bound to the root logger are affected by this method.
+    """
+    handlers = _get_library_root_logger().handlers
+
+    for handler in handlers:
+        formatter = logging.Formatter(
+            "[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"
+        )
+        handler.setFormatter(formatter)
+
+
+def reset_format() -> None:
+    """
+    Resets the formatting for 🤗 Diffusers' loggers.
+
+    All handlers currently bound to the root logger are affected by this method.
+    """
+    handlers = _get_library_root_logger().handlers
+
+    for handler in handlers:
+        handler.setFormatter(None)
+
+
+def warning_advice(self, *args, **kwargs) -> None:
+    """
+    This method is identical to `logger.warning()`, but if env var LATEXTURE_NO_ADVISORY_WARNINGS=1 is set, this
+    warning will not be printed
+    """
+    no_advisory_warnings = os.getenv("LATEXTURE_NO_ADVISORY_WARNINGS", False)
+    if no_advisory_warnings:
+        return
+    self.warning(*args, **kwargs)
+
+
+logging.Logger.warning_advice = warning_advice
+
+
+class EmptyTqdm:
+    """Dummy tqdm which doesn't do anything."""
+
+    def __init__(self, *args, **kwargs):  # pylint: disable=unused-argument
+        self._iterator = args[0] if args else None
+
+    def __iter__(self):
+        return iter(self._iterator)
+
+    def __getattr__(self, _):
+        """Return empty function."""
+
+        def empty_fn(*args, **kwargs):  # pylint: disable=unused-argument
+            return
+
+        return empty_fn
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type_, value, traceback):
+        return
+
+
+class _tqdm_cls:
+    def __call__(self, *args, **kwargs):
+        if _tqdm_active:
+            return tqdm_lib.tqdm(*args, **kwargs)
+        else:
+            return EmptyTqdm(*args, **kwargs)
+
+    def set_lock(self, *args, **kwargs):
+        self._lock = None
+        if _tqdm_active:
+            return tqdm_lib.tqdm.set_lock(*args, **kwargs)
+
+    def get_lock(self):
+        if _tqdm_active:
+            return tqdm_lib.tqdm.get_lock()
+
+
+tqdm = _tqdm_cls()
+
+
+def is_progress_bar_enabled() -> bool:
+    """Return a boolean indicating whether tqdm progress bars are enabled."""
+    global _tqdm_active
+    return bool(_tqdm_active)
+
+
+def enable_progress_bar() -> None:
+    """Enable tqdm progress bar."""
+    global _tqdm_active
+    _tqdm_active = True
+
+
+def disable_progress_bar() -> None:
+    """Disable tqdm progress bar."""
+    global _tqdm_active
+    _tqdm_active = False
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/__init__.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/__init__.py
new file mode 100644
index 0000000..dd4bbde
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/__init__.py
@@ -0,0 +1,20 @@
+from .camera import (
+    Camera,
+    get_c2w,
+    get_camera,
+    get_orthogonal_camera,
+    get_orthogonal_projection_matrix,
+    get_projection_matrix,
+)
+from .mesh import TexturedMesh, load_mesh, replace_mesh_texture_and_save
+from .projection import CameraProjection, CameraProjectionOutput
+from .render import (
+    DepthControlNetNormalization,
+    DepthNormalizationStrategy,
+    NVDiffRastContextWrapper,
+    RenderOutput,
+    SimpleNormalization,
+    Zero123PlusPlusNormalization,
+    render,
+)
+from .smart_paint import SmartPainter
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/blend.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/blend.py
new file mode 100755
index 0000000..34f830d
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/blend.py
@@ -0,0 +1,324 @@
+import time
+from abc import ABC, abstractmethod
+
+import torch
+import torch.nn.functional as F
+from torch.utils.cpp_extension import load_inline
+
+from .utils import SINGLE_IMAGE_TYPE, image_to_tensor
+
+pb_solver_cpp_source = """
+#include <torch/extension.h>
+
+#include <vector>
+
+// CUDA forward declarations
+
+void pb_solver_run_cuda(
+    torch::Tensor A,
+    torch::Tensor X,
+    torch::Tensor B,
+    torch::Tensor Xbuf,
+    int num_iters
+);
+
+// C++ interface
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+void pb_solver_run(
+    torch::Tensor A,
+    torch::Tensor X,
+    torch::Tensor B,
+    torch::Tensor Xbuf,
+    int num_iters
+) {
+    CHECK_INPUT(A);
+    CHECK_INPUT(X);
+    CHECK_INPUT(B);
+    CHECK_INPUT(Xbuf);
+
+    pb_solver_run_cuda(A, X, B, Xbuf, num_iters);
+    return;
+}
+
+"""
+
+pb_solver_cuda_source = """
+#include <torch/extension.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <algorithm>
+
+
+__global__ void pb_solver_run_cuda_kernel(
+    torch::PackedTensorAccessor32<int64_t,2,torch::RestrictPtrTraits> A,
+    torch::PackedTensorAccessor32<float,1,torch::RestrictPtrTraits> X,
+    torch::PackedTensorAccessor32<float,1,torch::RestrictPtrTraits> B,
+    torch::PackedTensorAccessor32<float,1,torch::RestrictPtrTraits> Xbuf
+) {
+    const int index = blockIdx.x * blockDim.x + threadIdx.x;
+    
+    if (index < A.size(0)) {
+        Xbuf[index] = (X[A[index][0]] + X[A[index][1]] + X[A[index][2]] + X[A[index][3]] + B[index]) / 4.;
+    }
+}
+
+void pb_solver_run_cuda(
+    torch::Tensor A,
+    torch::Tensor X,
+    torch::Tensor B,
+    torch::Tensor Xbuf,
+    int num_iters
+) {
+    int batch_size = A.size(0);
+
+    const int threads = 1024;
+    const dim3 blocks((batch_size + threads - 1) / threads);
+    
+    auto A_ptr = A.packed_accessor32<int64_t,2,torch::RestrictPtrTraits>();
+    auto X_ptr = X.packed_accessor32<float,1,torch::RestrictPtrTraits>();
+    auto B_ptr = B.packed_accessor32<float,1,torch::RestrictPtrTraits>();
+    auto Xbuf_ptr = Xbuf.packed_accessor32<float,1,torch::RestrictPtrTraits>();
+
+    for (int i = 0; i < num_iters; ++i) {
+        pb_solver_run_cuda_kernel<<<blocks, threads>>>(
+            A_ptr,
+            X_ptr,
+            B_ptr,
+            Xbuf_ptr
+        );
+        cudaDeviceSynchronize();
+        std::swap(X_ptr, Xbuf_ptr);
+    }
+    // we may waste an iteration here, but it's fine
+    return;
+}
+"""
+
+
+class PBBackend(ABC):
+    def __init__(self, *args, **kwargs) -> None:
+        pass
+
+    def solve(self, num_iters, A, X, B, Xbuf) -> None:
+        pass
+
+
+try:
+    import triton
+    import triton.language as tl
+
+    @triton.jit
+    def pb_triton_step_kernel(
+        A_ptr,
+        X_ptr,
+        B_ptr,
+        Xbuf_ptr,
+        A_row_stride: tl.constexpr,
+        n_elements: tl.constexpr,
+        BLOCK_SIZE: tl.constexpr,
+    ):
+        pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0.
+        block_start = pid * BLOCK_SIZE
+        offsets = tl.arange(0, BLOCK_SIZE) + block_start
+
+        A_start_ptr = A_ptr + block_start * A_row_stride
+        A_ptrs = A_start_ptr + (
+            tl.arange(0, BLOCK_SIZE)[:, None] * A_row_stride
+            + tl.arange(0, A_row_stride)[None, :]
+        )
+        B_ptrs = B_ptr + offsets
+        Xbuf_ptrs = Xbuf_ptr + offsets
+
+        mask = offsets < n_elements
+
+        A = tl.load(A_ptrs, mask=mask[:, None], other=0)
+        X = tl.load(X_ptr + A)
+        B = tl.load(B_ptrs, mask=mask, other=0.0)
+
+        Xout = (tl.sum(X, axis=1) + B) / 4
+        tl.store(Xbuf_ptrs, Xout, mask=mask)
+
+except:
+    pb_triton_step_kernel = None
+
+
+class PBTorchCUDAKernelBackend(PBBackend):
+    def __init__(self) -> None:
+        self.kernel = load_inline(
+            name="pb_solver",
+            cpp_sources=[pb_solver_cpp_source],
+            cuda_sources=[pb_solver_cuda_source],
+            functions=["pb_solver_run"],
+            verbose=True,
+        )
+
+    def solve(self, num_iters, A, X, B, Xbuf) -> None:
+        self.kernel.pb_solver_run(A, X, B, Xbuf, num_iters)
+
+
+class PBTritonBackend(PBBackend):
+    def step(
+        self, X: torch.Tensor, A: torch.Tensor, B: torch.Tensor, Xbuf: torch.Tensor
+    ) -> None:
+        n_elements = X.shape[0]
+        grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
+        assert pb_triton_step_kernel is not None
+        pb_triton_step_kernel[grid](A, X, B, Xbuf, A.stride(0), n_elements, 1024)
+
+    def solve(self, num_iters, A, X, B, Xbuf) -> None:
+        for _ in range(num_iters):
+            self.step(X, A, B, Xbuf)
+            X, Xbuf = Xbuf, X
+
+
+class PBTorchNativeBackend(PBBackend):
+    def step(
+        self, X: torch.Tensor, A: torch.Tensor, B: torch.Tensor, Xbuf: torch.Tensor
+    ) -> None:
+        X.copy_(X[A].sum(-1).add_(B).div_(4))
+
+    def solve(self, num_iters, A, X, B, Xbuf) -> None:
+        for _ in range(num_iters):
+            self.step(X, A, B, Xbuf)
+
+
+class PoissonBlendingSolver:
+    def __init__(self, backend: str, device: str):
+        self.backend = backend
+        if backend == "torch-native":
+            self.pb_solver = PBTorchNativeBackend()
+        elif backend == "torch-cuda":
+            self.pb_solver = PBTorchCUDAKernelBackend()
+        elif backend == "triton":
+            self.pb_solver = PBTritonBackend()
+        else:
+            raise ValueError(f"Unknown backend: {backend}")
+
+        self.device = device
+        self.lap_kernel = torch.tensor(
+            [[0, -1, 0], [-1, 4, -1], [0, -1, 0]], device=device, dtype=torch.float32
+        ).view(1, 1, 3, 3)
+        self.lap_kernel4 = torch.tensor(
+            [
+                [[0, -1, 0], [0, 1, 0], [0, 0, 0]],
+                [[0, 0, 0], [0, 1, 0], [0, -1, 0]],
+                [[0, 0, 0], [-1, 1, 0], [0, 0, 0]],
+                [[0, 0, 0], [0, 1, -1], [0, 0, 0]],
+            ],
+            device=device,
+            dtype=torch.float32,
+        ).view(4, 1, 3, 3)
+        self.neighbor_kernel = torch.tensor(
+            [[0, 1, 0], [1, 0, 1], [0, 1, 0]], device=device, dtype=torch.float32
+        ).view(1, 1, 3, 3)
+
+    def __call__(
+        self,
+        src: SINGLE_IMAGE_TYPE,
+        mask: SINGLE_IMAGE_TYPE,
+        tgt: SINGLE_IMAGE_TYPE,
+        num_iters: int,
+        inplace: bool = True,
+        grad_mode: str = "src",
+    ):
+        src = image_to_tensor(src, device=self.device)
+        mask = image_to_tensor(mask, device=self.device)
+        tgt = image_to_tensor(tgt, device=self.device)
+
+        assert src.ndim == 3 and tgt.ndim == 3 and mask.ndim in [2, 3]
+
+        if mask.ndim == 3:
+            mask = mask.mean(-1) > 0.5
+        else:
+            mask = mask > 0.5
+        mask[0, :] = 0
+        mask[-1, :] = 0
+        mask[:, 0] = 0
+        mask[:, -1] = 0
+
+        tgt_masked = torch.where(mask[..., None], torch.zeros_like(tgt), tgt)
+
+        x, y = mask.nonzero(as_tuple=True)
+        N = x.shape[0]
+        index_map = torch.cumsum(mask.reshape(-1).long(), dim=-1).reshape(mask.shape)
+        index_map[~mask] = 0
+
+        if grad_mode == "src":
+            src_lap = F.conv2d(
+                src.permute(2, 0, 1)[:, None],
+                weight=self.lap_kernel,
+                padding=1,
+            )[:, 0].permute(1, 2, 0)
+            lap = src_lap
+        elif grad_mode == "max":
+            src_lap = F.conv2d(
+                src.permute(2, 0, 1)[:, None],
+                weight=self.lap_kernel4,
+                padding=1,
+            )
+            tgt_lap = F.conv2d(
+                tgt.permute(2, 0, 1)[:, None],
+                weight=self.lap_kernel4,
+                padding=1,
+            )
+            lap = (
+                torch.where(src_lap.abs() > tgt_lap.abs(), src_lap, tgt_lap)
+                .sum(1)
+                .permute(1, 2, 0)
+            )
+        elif grad_mode == "avg":
+            src_lap = F.conv2d(
+                src.permute(2, 0, 1)[:, None],
+                weight=self.lap_kernel4,
+                padding=1,
+            )
+            tgt_lap = F.conv2d(
+                tgt.permute(2, 0, 1)[:, None],
+                weight=self.lap_kernel4,
+                padding=1,
+            )
+            lap = (src_lap + tgt_lap).mul(0.5).sum(1).permute(1, 2, 0)
+
+        fq_star = F.conv2d(
+            tgt_masked.permute(2, 0, 1)[:, None],
+            weight=self.neighbor_kernel,
+            padding=1,
+        )[:, 0].permute(1, 2, 0)
+
+        A = torch.zeros(N + 1, 4, device=self.device, dtype=torch.long)
+        X = torch.zeros(N + 1, 3, device=self.device, dtype=torch.float32)
+        B = torch.zeros(N + 1, 3, device=self.device, dtype=torch.float32)
+
+        A[1:] = torch.stack(
+            [
+                index_map[x - 1, y],
+                index_map[x + 1, y],
+                index_map[x, y - 1],
+                index_map[x, y + 1],
+            ],
+            dim=-1,
+        )
+        X[1:] = tgt[x, y]
+        B[1:] = lap[x, y] + fq_star[x, y]
+
+        X_flatten = X.flatten()
+        B_flatten = B.flatten()
+        A_flatten = torch.stack([3 * A, 3 * A + 1, 3 * A + 2], dim=1).reshape(-1, 4)
+
+        buffer = torch.zeros_like(X_flatten)
+
+        self.pb_solver.solve(num_iters, A_flatten, X_flatten, B_flatten, buffer)
+
+        if inplace:
+            tgt[x, y] = X_flatten.view(-1, 3)[1:].clamp(0.0, 1.0)
+        else:
+            tgt = tgt.clone()
+            tgt[x, y] = X_flatten.view(-1, 3)[1:].clamp(0.0, 1.0)
+
+        return tgt
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/camera.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/camera.py
new file mode 100644
index 0000000..77d8bcf
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/camera.py
@@ -0,0 +1,223 @@
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import trimesh
+from PIL import Image
+from torch import BoolTensor, FloatTensor
+
+from .utils import LIST_TYPE
+
+
+def list_to_pt(
+    x: LIST_TYPE, dtype: Optional[torch.dtype] = None, device: Optional[str] = None
+) -> torch.Tensor:
+    if isinstance(x, list) or isinstance(x, np.ndarray):
+        return torch.tensor(x, dtype=dtype, device=device)
+    return x.to(dtype=dtype)
+
+
+def get_c2w(
+    elevation_deg: LIST_TYPE,
+    distance: LIST_TYPE,
+    azimuth_deg: Optional[LIST_TYPE],
+    num_views: Optional[int] = 1,
+    device: Optional[str] = None,
+) -> torch.FloatTensor:
+    if azimuth_deg is None:
+        assert (
+            num_views is not None
+        ), "num_views must be provided if azimuth_deg is None."
+        azimuth_deg = torch.linspace(
+            0, 360, num_views + 1, dtype=torch.float32, device=device
+        )[:-1]
+    else:
+        num_views = len(azimuth_deg)
+    azimuth_deg = list_to_pt(azimuth_deg, dtype=torch.float32, device=device)
+    elevation_deg = list_to_pt(elevation_deg, dtype=torch.float32, device=device)
+    camera_distances = list_to_pt(distance, dtype=torch.float32, device=device)
+    elevation = elevation_deg * math.pi / 180
+    azimuth = azimuth_deg * math.pi / 180
+    camera_positions = torch.stack(
+        [
+            camera_distances * torch.cos(elevation) * torch.cos(azimuth),
+            camera_distances * torch.cos(elevation) * torch.sin(azimuth),
+            camera_distances * torch.sin(elevation),
+        ],
+        dim=-1,
+    )
+    center = torch.zeros_like(camera_positions)
+    up = torch.tensor([0, 0, 1], dtype=torch.float32, device=device)[None, :].repeat(
+        num_views, 1
+    )
+    lookat = F.normalize(center - camera_positions, dim=-1)
+    right = F.normalize(torch.cross(lookat, up, dim=-1), dim=-1)
+    up = F.normalize(torch.cross(right, lookat, dim=-1), dim=-1)
+    c2w3x4 = torch.cat(
+        [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
+        dim=-1,
+    )
+    c2w = torch.cat([c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1)
+    c2w[:, 3, 3] = 1.0
+    return c2w
+
+
+def get_projection_matrix(
+    fovy_deg: LIST_TYPE,
+    aspect_wh: float = 1.0,
+    near: float = 0.1,
+    far: float = 100.0,
+    device: Optional[str] = None,
+) -> torch.FloatTensor:
+    fovy_deg = list_to_pt(fovy_deg, dtype=torch.float32, device=device)
+    batch_size = fovy_deg.shape[0]
+    fovy = fovy_deg * math.pi / 180
+    tan_half_fovy = torch.tan(fovy / 2)
+    projection_matrix = torch.zeros(
+        batch_size, 4, 4, dtype=torch.float32, device=device
+    )
+    projection_matrix[:, 0, 0] = 1 / (aspect_wh * tan_half_fovy)
+    projection_matrix[:, 1, 1] = -1 / tan_half_fovy
+    projection_matrix[:, 2, 2] = -(far + near) / (far - near)
+    projection_matrix[:, 2, 3] = -2 * far * near / (far - near)
+    projection_matrix[:, 3, 2] = -1
+    return projection_matrix
+
+
+def get_orthogonal_projection_matrix(
+    batch_size: int,
+    left: float,
+    right: float,
+    bottom: float,
+    top: float,
+    near: float = 0.1,
+    far: float = 100.0,
+    device: Optional[str] = None,
+) -> torch.FloatTensor:
+    projection_matrix = torch.zeros(
+        batch_size, 4, 4, dtype=torch.float32, device=device
+    )
+    projection_matrix[:, 0, 0] = 2 / (right - left)
+    projection_matrix[:, 1, 1] = -2 / (top - bottom)
+    projection_matrix[:, 2, 2] = -2 / (far - near)
+    projection_matrix[:, 0, 3] = -(right + left) / (right - left)
+    projection_matrix[:, 1, 3] = -(top + bottom) / (top - bottom)
+    projection_matrix[:, 2, 3] = -(far + near) / (far - near)
+    projection_matrix[:, 3, 3] = 1
+    return projection_matrix
+
+
+@dataclass
+class Camera:
+    c2w: Optional[torch.FloatTensor]
+    w2c: torch.FloatTensor
+    proj_mtx: torch.FloatTensor
+    mvp_mtx: torch.FloatTensor
+    cam_pos: Optional[torch.FloatTensor]
+
+    def __getitem__(self, index):
+        if isinstance(index, int):
+            sl = slice(index, index + 1)
+        elif isinstance(index, slice):
+            sl = index
+        elif isinstance(index, list):
+            sl = index
+        else:
+            raise NotImplementedError
+
+        return Camera(
+            c2w=self.c2w[sl] if self.c2w is not None else None,
+            w2c=self.w2c[sl],
+            proj_mtx=self.proj_mtx[sl],
+            mvp_mtx=self.mvp_mtx[sl],
+            cam_pos=self.cam_pos[sl] if self.cam_pos is not None else None,
+        )
+
+    def to(self, device: Optional[str] = None):
+        if self.c2w is not None:
+            self.c2w = self.c2w.to(device)
+        self.w2c = self.w2c.to(device)
+        self.proj_mtx = self.proj_mtx.to(device)
+        self.mvp_mtx = self.mvp_mtx.to(device)
+        if self.cam_pos is not None:
+            self.cam_pos = self.cam_pos.to(device)
+
+    def __len__(self):
+        return self.c2w.shape[0]
+
+
+def get_camera(
+    elevation_deg: Optional[LIST_TYPE] = None,
+    distance: Optional[LIST_TYPE] = None,
+    fovy_deg: Optional[LIST_TYPE] = None,
+    azimuth_deg: Optional[LIST_TYPE] = None,
+    num_views: Optional[int] = 1,
+    c2w: Optional[torch.FloatTensor] = None,
+    w2c: Optional[torch.FloatTensor] = None,
+    proj_mtx: Optional[torch.FloatTensor] = None,
+    aspect_wh: float = 1.0,
+    near: float = 0.1,
+    far: float = 100.0,
+    perturb_camera_position: Optional[float] = None,
+    device: Optional[str] = None,
+):
+    if w2c is None:
+        if c2w is None:
+            c2w = get_c2w(elevation_deg, distance, azimuth_deg, num_views, device)
+            if perturb_camera_position is not None:
+                perturbed_pos = (
+                    c2w[:, :3, 3]
+                    + torch.randn_like(c2w[:, :3, 3]) * perturb_camera_position
+                )
+                perturbed_pos = (
+                    F.normalize(perturbed_pos, dim=-1)
+                    * ((c2w[:, :3, 3] ** 2).sum(-1) ** 0.5)[:, None]
+                )
+        camera_positions = c2w[:, :3, 3]
+        w2c = torch.linalg.inv(c2w)
+    else:
+        camera_positions = None
+        c2w = None
+    if proj_mtx is None:
+        proj_mtx = get_projection_matrix(
+            fovy_deg, aspect_wh=aspect_wh, near=near, far=far, device=device
+        )
+    mvp_mtx = proj_mtx @ w2c
+    return Camera(
+        c2w=c2w, w2c=w2c, proj_mtx=proj_mtx, mvp_mtx=mvp_mtx, cam_pos=camera_positions
+    )
+
+
+def get_orthogonal_camera(
+    elevation_deg: LIST_TYPE,
+    distance: LIST_TYPE,
+    left: float,
+    right: float,
+    bottom: float,
+    top: float,
+    azimuth_deg: Optional[LIST_TYPE] = None,
+    num_views: Optional[int] = 1,
+    near: float = 0.1,
+    far: float = 100.0,
+    device: Optional[str] = None,
+):
+    c2w = get_c2w(elevation_deg, distance, azimuth_deg, num_views, device)
+    camera_positions = c2w[:, :3, 3]
+    w2c = torch.linalg.inv(c2w)
+    proj_mtx = get_orthogonal_projection_matrix(
+        batch_size=c2w.shape[0],
+        left=left,
+        right=right,
+        bottom=bottom,
+        top=top,
+        near=near,
+        far=far,
+        device=device,
+    )
+    mvp_mtx = proj_mtx @ w2c
+    return Camera(
+        c2w=c2w, w2c=w2c, proj_mtx=proj_mtx, mvp_mtx=mvp_mtx, cam_pos=camera_positions
+    )
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/cv_ops.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/cv_ops.py
new file mode 100644
index 0000000..b3cb936
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/cv_ops.py
@@ -0,0 +1,191 @@
+from typing import Optional
+
+
+import torch
+import torch.nn.functional as F
+
+try:
+    import cvcuda
+    torch_to_cvc = lambda x, layout: cvcuda.as_tensor(x, layout)
+
+    cvc_to_torch = lambda x, device: torch.tensor(x.cuda(), device=device)
+except ImportError:
+    cvcuda = None
+    torch_to_cvc = None
+    cvc_to_torch = None
+
+
+def inpaint_cvc(
+    image: torch.Tensor,
+    mask: torch.Tensor,
+    padding_size: int,
+    return_dtype: Optional[torch.dtype] = None,
+):
+    input_dtype = image.dtype
+    input_device = image.device
+
+    image = image.detach()
+    mask = mask.detach()
+
+    if image.dtype != torch.uint8:
+        image = (image * 255).to(torch.uint8)
+    if mask.dtype != torch.uint8:
+        mask = (mask * 255).to(torch.uint8)
+
+    image_cvc = torch_to_cvc(image, "HWC")
+    mask_cvc = torch_to_cvc(mask, "HW")
+    output_cvc = cvcuda.inpaint(image_cvc, mask_cvc, padding_size)
+    output = cvc_to_torch(output_cvc, device=input_device)
+
+    if return_dtype == torch.uint8 or input_dtype == torch.uint8:
+        return output
+    return output.to(dtype=input_dtype) / 255.0
+
+
+def inpaint_torch(
+    image: torch.Tensor,
+    mask: torch.Tensor,
+    padding_size: int,
+    return_dtype: Optional[torch.dtype] = None,
+):
+    """
+    Inpaint masked areas in an image using PyTorch operations.
+
+    Args:
+        image: Input image tensor [H, W, C]
+        mask: Binary mask tensor [H, W] where non-zero values indicate pixels to inpaint
+        padding_size: Size of neighborhood to consider for inpainting
+        return_dtype: Optional dtype for the output tensor
+
+    Returns:
+        Inpainted image tensor
+    """
+    input_dtype = image.dtype
+    input_device = image.device
+
+    image = image.detach()
+    mask = mask.detach()
+
+    if image.dtype != torch.uint8:
+        image = (image * 255).to(torch.uint8)
+    if mask.dtype != torch.uint8:
+        mask = (mask * 255).to(torch.uint8)
+
+    # Convert to float for processing
+    image_float = image.float() / 255.0
+    mask_float = (mask > 0).float()  # 1 for areas to inpaint, 0 for known areas
+
+    # Initialize output with original image
+    output = image_float.clone()
+
+    # Create distance-based weights for neighborhood pixels
+    kernel_size = min(2 * padding_size + 1, 31)  # Limit kernel size to avoid memory issues
+    y, x = torch.meshgrid(
+        torch.arange(-(kernel_size // 2), kernel_size // 2 + 1, device=input_device),
+        torch.arange(-(kernel_size // 2), kernel_size // 2 + 1, device=input_device),
+        indexing="ij"
+    )
+    dist = torch.sqrt(x.float() ** 2 + y.float() ** 2)
+    weights = 1.0 / (dist + 1e-6)
+    weights = weights / weights.sum()
+    weights_kernel = weights.unsqueeze(0).unsqueeze(0)
+
+    # For each channel, compute weighted average of non-masked pixels
+    inpainted = []
+
+    for c in range(image_float.shape[-1]):
+        channel = image_float[..., c]
+
+        # Compute weighted sum of valid pixels
+        valid_values = channel * (1 - mask_float)
+        weighted_sum = F.conv2d(
+            valid_values.unsqueeze(0).unsqueeze(0),
+            weights_kernel,
+            padding=kernel_size // 2
+        ).squeeze(0).squeeze(0)
+
+        # Compute sum of weights for valid pixels
+        valid_weights = F.conv2d(
+            (1 - mask_float).unsqueeze(0).unsqueeze(0),
+            weights_kernel,
+            padding=kernel_size // 2
+        ).squeeze(0).squeeze(0)
+
+        # Avoid division by zero
+        valid_weights = torch.clamp(valid_weights, min=1e-6)
+
+        # Compute weighted average
+        inpainted_channel = weighted_sum / valid_weights
+
+        # Only update masked pixels
+        inpainted_channel = torch.where(mask_float > 0, inpainted_channel, channel)
+        inpainted.append(inpainted_channel)
+
+    # Combine channels
+    output = torch.stack(inpainted, dim=-1)
+
+    # Convert back to appropriate dtype
+    if return_dtype == torch.uint8 or input_dtype == torch.uint8:
+        output = (output * 255).to(torch.uint8)
+    else:
+        output = output.to(dtype=input_dtype)
+
+    return output
+
+
+def batch_inpaint_cvc(
+    images: torch.Tensor,
+    masks: torch.Tensor,
+    padding_size: int,
+    return_dtype: Optional[torch.dtype] = None,
+):
+    output = torch.stack(
+        [
+            inpaint_cvc(image, mask, padding_size, return_dtype)
+            for (image, mask) in zip(images, masks)
+        ],
+        axis=0,
+    )
+    return output
+
+
+def batch_erode(
+    masks: torch.Tensor, kernel_size: int, return_dtype: Optional[torch.dtype] = None
+):
+    input_dtype = masks.dtype
+    input_device = masks.device
+    masks = masks.detach()
+    if masks.dtype != torch.uint8:
+        masks = (masks.float() * 255).to(torch.uint8)
+    masks_cvc = torch_to_cvc(masks[..., None], "NHWC")
+    masks_erode_cvc = cvcuda.morphology(
+        masks_cvc,
+        cvcuda.MorphologyType.ERODE,
+        maskSize=(kernel_size, kernel_size),
+        anchor=(-1, -1),
+    )
+    masks_erode = cvc_to_torch(masks_erode_cvc, device=input_device)[..., 0]
+    if return_dtype == torch.uint8 or input_dtype == torch.uint8:
+        return masks_erode
+    return (masks_erode > 0).to(dtype=input_dtype)
+
+
+def batch_dilate(
+    masks: torch.Tensor, kernel_size: int, return_dtype: Optional[torch.dtype] = None
+):
+    input_dtype = masks.dtype
+    input_device = masks.device
+    masks = masks.detach()
+    if masks.dtype != torch.uint8:
+        masks = (masks.float() * 255).to(torch.uint8)
+    masks_cvc = torch_to_cvc(masks[..., None], "NHWC")
+    masks_dilate_cvc = cvcuda.morphology(
+        masks_cvc,
+        cvcuda.MorphologyType.DILATE,
+        maskSize=(kernel_size, kernel_size),
+        anchor=(-1, -1),
+    )
+    masks_dilate = cvc_to_torch(masks_dilate_cvc, device=input_device)[..., 0]
+    if return_dtype == torch.uint8 or input_dtype == torch.uint8:
+        return masks_dilate
+    return (masks_dilate > 0).to(dtype=input_dtype)
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/mesh.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/mesh.py
new file mode 100644
index 0000000..8b4efde
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/mesh.py
@@ -0,0 +1,522 @@
+import io
+import math
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List, Optional, Union
+
+import numpy as np
+import nvdiffrast.torch as dr
+import torch
+import torch.nn.functional as F
+import trimesh
+from PIL import Image
+from torch import BoolTensor, FloatTensor
+
+from .utils import tensor_to_image
+
+
+def dot(x: torch.FloatTensor, y: torch.FloatTensor) -> torch.FloatTensor:
+    return torch.sum(x * y, -1, keepdim=True)
+
+
+@dataclass
+class TexturedMesh:
+    v_pos: torch.FloatTensor
+    t_pos_idx: torch.LongTensor
+
+    # texture coordinates
+    v_tex: Optional[torch.FloatTensor] = None
+    t_tex_idx: Optional[torch.LongTensor] = None
+
+    # texture map
+    texture: Optional[torch.FloatTensor] = None
+
+    # vertices, faces after vertex merging
+    _stitched_v_pos: Optional[torch.FloatTensor] = None
+    _stitched_t_pos_idx: Optional[torch.LongTensor] = None
+
+    _v_nrm: Optional[torch.FloatTensor] = None
+    _v_tang: Optional[torch.FloatTensor] = None
+
+    @property
+    def v_nrm(self) -> torch.FloatTensor:
+        if self._v_nrm is None:
+            self._v_nrm = self._compute_vertex_normal()
+        return self._v_nrm
+
+    @property
+    def v_tang(self) -> torch.FloatTensor:
+        if self._v_tang is None:
+            self._v_tang = self._compute_tangent()
+        return self._v_tang
+
+    def set_vertex_normal(self, v_nrm: torch.FloatTensor) -> None:
+        assert v_nrm.shape == self.v_pos.shape
+        self._v_nrm = v_nrm.to(self.v_pos)
+
+    def set_stitched_mesh(
+        self, v_pos: torch.FloatTensor, t_pos_idx: torch.LongTensor
+    ) -> None:
+        self._stitched_v_pos = v_pos
+        self._stitched_t_pos_idx = t_pos_idx
+
+    @property
+    def stitched_v_pos(self) -> torch.FloatTensor:
+        if self._stitched_v_pos is None:
+            print("Warning: Stitched vertices not available, using original vertices!")
+            return self.v_pos
+        return self._stitched_v_pos
+
+    @property
+    def stitched_t_pos_idx(self) -> torch.LongTensor:
+        if self._stitched_t_pos_idx is None:
+            print("Warning: Stitched faces not available, using original faces!")
+            return self.t_pos_idx
+        return self._stitched_t_pos_idx
+
+    @property
+    def uv_size(self) -> Optional[int]:
+        if self.texture is None:
+            return None
+        return self.texture.shape[0]
+
+    def _compute_vertex_normal(self) -> torch.FloatTensor:
+        if self._stitched_v_pos is None or self._stitched_t_pos_idx is None:
+            print(
+                "Warning: Stitched vertices and faces not available, computing vertex normals on original mesh, which can be erroneous!"
+            )
+            v_pos, t_pos_idx = self.v_pos, self.t_pos_idx
+        else:
+            v_pos, t_pos_idx = self._stitched_v_pos, self._stitched_t_pos_idx
+
+        i0 = t_pos_idx[:, 0]
+        i1 = t_pos_idx[:, 1]
+        i2 = t_pos_idx[:, 2]
+
+        v0 = v_pos[i0, :]
+        v1 = v_pos[i1, :]
+        v2 = v_pos[i2, :]
+
+        face_normals = torch.cross(v1 - v0, v2 - v0)
+
+        # Splat face normals to vertices
+        v_nrm = torch.zeros_like(v_pos)
+        v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
+        v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
+        v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
+
+        # Normalize, replace zero (degenerated) normals with some default value
+        v_nrm = torch.where(
+            dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
+        )
+        v_nrm = F.normalize(v_nrm, dim=1)
+
+        if torch.is_anomaly_enabled():
+            assert torch.all(torch.isfinite(v_nrm))
+
+        return v_nrm
+
+    def _compute_tangent(self) -> torch.FloatTensor:
+        vn_idx = [None] * 3
+        pos = [None] * 3
+        tex = [None] * 3
+        for i in range(0, 3):
+            pos[i] = self.v_pos[self.t_pos_idx[:, i]]
+            tex[i] = self.v_tex[self.t_tex_idx[:, i]]
+            vn_idx[i] = self.t_pos_idx[:, i]
+
+        tangents = torch.zeros_like(self._v_nrm)
+        tansum = torch.zeros_like(self._v_nrm)
+
+        # Compute tangent space for each triangle
+        uve1 = tex[1] - tex[0]
+        uve2 = tex[2] - tex[0]
+        pe1 = pos[1] - pos[0]
+        pe2 = pos[2] - pos[0]
+
+        nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]
+        denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]
+
+        # Avoid division by zero for degenerated texture coordinates
+        tang = nom / torch.where(
+            denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)
+        )
+
+        # Update all 3 vertices
+        for i in range(0, 3):
+            idx = vn_idx[i][:, None].repeat(1, 3)
+            tangents.scatter_add_(0, idx, tang)  # tangents[n_i] = tangents[n_i] + tang
+            tansum.scatter_add_(
+                0, idx, torch.ones_like(tang)
+            )  # tansum[n_i] = tansum[n_i] + 1
+        tangents = tangents / tansum
+
+        # Normalize and make sure tangent is perpendicular to normal
+        tangents = F.normalize(tangents, dim=1)
+        tangents = F.normalize(
+            tangents - dot(tangents, self._v_nrm) * self._v_nrm, dim=1
+        )
+
+        v_tng = tangents
+
+        if torch.is_anomaly_enabled():
+            assert torch.all(torch.isfinite(v_tng))
+
+        return v_tng
+
+    def to(self, device: Optional[str] = None):
+        self.v_pos = self.v_pos.to(device)
+        self.t_pos_idx = self.t_pos_idx.to(device)
+        if self.v_tex is not None:
+            self.v_tex = self.v_tex.to(device)
+        if self.t_tex_idx is not None:
+            self.t_tex_idx = self.t_tex_idx.to(device)
+        if self.texture is not None:
+            self.texture = self.texture.to(device)
+        if self._stitched_v_pos is not None:
+            self._stitched_v_pos = self._stitched_v_pos.to(device)
+        if self._stitched_t_pos_idx is not None:
+            self._stitched_t_pos_idx = self._stitched_t_pos_idx.to(device)
+        if self._v_nrm is not None:
+            self._v_nrm = self._v_nrm.to(device)
+        if self._v_tang is not None:
+            self._v_tang = self._v_tang.to(device)
+
+
+@contextmanager
+def mesh_use_texture(mesh: TexturedMesh, texture: torch.FloatTensor):
+    texture_ = mesh.texture
+    mesh.texture = texture
+    try:
+        yield
+    finally:
+        mesh.texture = texture_
+
+
+def load_mesh(
+    mesh_path: str,
+    rescale: bool = False,
+    move_to_center: bool = False,
+    scale: float = 0.5,
+    flip_uv: bool = True,
+    merge_vertices: bool = True,
+    default_uv_size: Optional[int] = None,
+    shape_init_mesh_up: str = "+y",
+    shape_init_mesh_front: str = "+x",
+    front_x_to_y: bool = False,
+    device: Optional[str] = None,
+    return_transform: bool = False,
+) -> TexturedMesh:
+    if mesh_path.endswith(".npz"):
+
+        class Mesh:
+            vertices = None
+            faces = None
+
+        data = np.load(mesh_path)
+        mesh = Mesh()
+        mesh.vertices = data["vertices"]
+        mesh.faces = data["faces"]
+        merge_vertices = False
+    else:
+        scene = trimesh.load(mesh_path, force="mesh", process=False)
+        if isinstance(scene, trimesh.Trimesh):
+            mesh = scene
+        elif isinstance(scene, trimesh.scene.Scene):
+            mesh = trimesh.Trimesh()
+            for obj in scene.geometry.values():
+                mesh = trimesh.util.concatenate([mesh, obj])
+        else:
+            raise ValueError(f"Unknown mesh type at {mesh_path}.")
+
+    vertex_normals = getattr(mesh, "vertex_normals", None)
+
+    # move to center
+    transform_offset = None
+    if move_to_center:
+        centroid = mesh.vertices.mean(0)
+        mesh.vertices = mesh.vertices - centroid
+        transform_offset = centroid  # record the offset
+
+    # rescale
+    transform_scale = None
+    if rescale:
+        max_scale = np.abs(mesh.vertices).max()
+        mesh.vertices = mesh.vertices / max_scale * scale
+        transform_scale = max_scale / scale
+
+    dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
+    dir2vec = {
+        "+x": np.array([1, 0, 0]),
+        "+y": np.array([0, 1, 0]),
+        "+z": np.array([0, 0, 1]),
+        "-x": np.array([-1, 0, 0]),
+        "-y": np.array([0, -1, 0]),
+        "-z": np.array([0, 0, -1]),
+    }
+    if shape_init_mesh_up not in dirs or shape_init_mesh_front not in dirs:
+        raise ValueError(
+            f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
+        )
+    if shape_init_mesh_up[1] == shape_init_mesh_front[1]:
+        raise ValueError(
+            "shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
+        )
+    z_, x_ = (
+        dir2vec[shape_init_mesh_up],
+        dir2vec[shape_init_mesh_front],
+    )
+    y_ = np.cross(z_, x_)
+    std2mesh = np.stack([x_, y_, z_], axis=0).T
+    mesh2std = np.linalg.inv(std2mesh)
+    mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
+    if vertex_normals is not None:
+        vertex_normals = np.dot(mesh2std, vertex_normals.T).T
+    if front_x_to_y:
+        x = mesh.vertices[:, 1].copy()
+        y = -mesh.vertices[:, 0].copy()
+        mesh.vertices[:, 0] = x
+        mesh.vertices[:, 1] = y
+        if vertex_normals is not None:
+            vx = vertex_normals[:, 1].copy()
+            vy = -vertex_normals[:, 0].copy()
+            vertex_normals[:, 0] = vx
+            vertex_normals[:, 1] = vy
+
+    v_pos = torch.tensor(mesh.vertices, dtype=torch.float32)
+    t_pos_idx = torch.tensor(mesh.faces, dtype=torch.int64)
+
+    if hasattr(mesh, "visual") and hasattr(mesh.visual, "uv"):
+        v_tex = torch.tensor(mesh.visual.uv, dtype=torch.float32)
+        if flip_uv:
+            v_tex[:, 1] = 1.0 - v_tex[:, 1]
+        t_tex_idx = t_pos_idx.clone()
+        if (
+            default_uv_size is not None
+            or getattr(mesh.visual.material, "baseColorTexture", None) is None
+        ):
+            assert default_uv_size is not None
+            texture = torch.zeros(
+                (default_uv_size, default_uv_size, 3), dtype=torch.float32
+            )
+        else:
+            texture = torch.tensor(
+                np.array(mesh.visual.material.baseColorTexture) / 255.0,
+                dtype=torch.float32,
+            )[..., :3]
+    else:
+        v_tex = None
+        t_tex_idx = None
+        texture = None
+
+    textured_mesh = TexturedMesh(
+        v_pos=v_pos,
+        t_pos_idx=t_pos_idx,
+        v_tex=v_tex,
+        t_tex_idx=t_tex_idx,
+        texture=texture,
+    )
+
+    if vertex_normals is not None:
+        v_nrm = F.normalize(torch.tensor(vertex_normals, dtype=torch.float32), dim=-1)
+        textured_mesh.set_vertex_normal(v_nrm)
+
+    if merge_vertices and vertex_normals is None:
+        # only merge vertices when vertex normals are not available
+        mesh.merge_vertices(merge_tex=True)
+        textured_mesh.set_stitched_mesh(
+            torch.tensor(mesh.vertices, dtype=torch.float32),
+            torch.tensor(mesh.faces, dtype=torch.int64),
+        )
+    else:
+        textured_mesh.set_stitched_mesh(textured_mesh.v_pos, textured_mesh.t_pos_idx)
+
+    textured_mesh.to(device)
+
+    if return_transform:
+        return textured_mesh, transform_offset, transform_scale
+    else:
+        return textured_mesh
+
+
+def replace_mesh_texture_and_save_trimesh(
+    input_path: str,
+    output_path: str,
+    texture: Union[Image.Image, np.ndarray, torch.Tensor],
+    metallic_roughness_texture: Optional[
+        Union[Image.Image, np.ndarray, torch.Tensor]
+    ] = None,
+    normal_texture: Optional[Union[Image.Image, np.ndarray, torch.Tensor]] = None,
+    texture_format: str = "JPEG",
+    task_id: str = "",
+    **kwargs,
+) -> None:
+    tmesh = trimesh.load(input_path, force="mesh", process=False)
+
+    def convert_image(tensor):
+        texture = tensor_to_image(tensor)
+        texture.format = texture_format
+        return texture
+
+    texture = convert_image(texture)
+    if metallic_roughness_texture is not None:
+        metallic_roughness_texture = convert_image(metallic_roughness_texture)
+
+    if normal_texture is not None:
+        normal_texture = convert_image(normal_texture)
+
+    if not isinstance(tmesh.visual.material, trimesh.visual.material.PBRMaterial):
+        material = trimesh.visual.texture.PBRMaterial(
+            baseColorFactor=[1.0, 1.0, 1.0, 1.0],
+            roughnessFactor=0.9 if metallic_roughness_texture is None else 1.0,
+            metallicFactor=0.0 if metallic_roughness_texture is None else 1.0,
+            baseColorTexture=texture,
+            metallicRoughnessTexture=metallic_roughness_texture,
+            normalTexture=normal_texture,
+        )
+        tmesh.visual = trimesh.visual.TextureVisuals(
+            uv=tmesh.visual.uv, image=texture, material=material
+        )
+    else:
+        tmesh.visual.material.baseColorTexture = texture
+        tmesh.visual.material.baseColorFactor = [1.0, 1.0, 1.0, 1.0]
+        if metallic_roughness_texture is None:
+            tmesh.visual.material.roughnessFactor = 0.9
+            tmesh.visual.material.metallicFactor = 0.0
+        else:
+            tmesh.visual.material.roughnessFactor = 1.0
+            tmesh.visual.material.metallicFactor = 1.0
+            tmesh.visual.material.metallicRoughnessTexture = metallic_roughness_texture
+            tmesh.visual.material.normalTexture = normal_texture
+
+    def p_func(tree, task_id_in=task_id):
+        if "nodes" in tree and tree["nodes"][0]["name"].find("world") >= 0:
+            tree["nodes"].pop(0)
+        tree["asset"]["generator"] = "https://github.com/huanngzh/MV-Adapter"
+        change_dict = {
+            "nodes": "node",
+            "meshes": "mesh",
+            "materials": "mat",
+            "images": "image",
+        }
+        for k, v in change_dict.items():
+            if k in tree:
+                for index in range(len(tree[k])):
+                    tree[k][index]["name"] = f"mvadapter_{v}_{task_id_in}"
+
+    tmesh.export(output_path, tree_postprocessor=p_func)
+
+
+# def replace_mesh_texture_and_save_gltflib(
+#     input_path: str,
+#     output_path: str,
+#     texture: Union[Image.Image, np.ndarray, torch.Tensor],
+#     metallic_roughness_texture: Optional[
+#         Union[Image.Image, np.ndarray, torch.Tensor]
+#     ] = None,
+#     normal_texture: Optional[Union[Image.Image, np.ndarray, torch.Tensor]] = None,
+#     texture_format: str = "JPEG",
+#     normal_strength: float = 1.0,
+#     task_id: str = "",
+#     **kwargs,
+# ) -> None:
+#     from gltflib import GLTF
+#     from gltflib import Image as gltfImage
+#     from gltflib import NormalTextureInfo, Texture, TextureInfo
+
+#     def convert_image(tensor):
+#         texture = tensor_to_image(tensor)
+#         texture.format = texture_format
+#         return texture
+
+#     def add_texture(gltf: GLTF, image: Image.Image) -> int:
+#         jpeg_buffer = io.BytesIO()
+#         image.save(jpeg_buffer, format=texture_format)
+#         new_texture_data = bytearray(jpeg_buffer.getvalue())
+#         _, offset, bytelen = gltf._create_or_extend_glb_resource(new_texture_data)
+#         bufferView = gltf._create_embedded_image_buffer_view(offset, bytelen)
+#         image = gltfImage(mimeType="image/jpeg", bufferView=bufferView)
+#         if gltf.model.images:
+#             gltf.model.images.append(image)
+#         else:
+#             gltf.model.images = [image]
+#         texture = Texture(source=len(gltf.model.images) - 1)
+#         if gltf.model.textures:
+#             gltf.model.textures.append(texture)
+#         else:
+#             gltf.model.textures = [texture]
+#         return len(gltf.model.textures) - 1
+
+#     gltf = GLTF.load(input_path)
+
+#     texture = convert_image(texture)
+#     if metallic_roughness_texture is not None:
+#         metallic_roughness_texture = convert_image(metallic_roughness_texture)
+
+#     if normal_texture is not None:
+#         normal_texture = convert_image(normal_texture)
+
+#     pbr_material = gltf.model.materials[0].pbrMetallicRoughness
+
+#     pbr_material.baseColorTexture = TextureInfo(index=add_texture(gltf, texture))
+#     pbr_material.baseColorFactor = [1, 1, 1, 1]
+
+#     if metallic_roughness_texture is not None:
+#         pbr_material.metallicRoughnessTexture = TextureInfo(
+#             index=add_texture(gltf, metallic_roughness_texture)
+#         )
+#         pbr_material.metallicFactor = 1.0
+#         pbr_material.roughnessFactor = 1.0
+#     else:
+#         pbr_material.metallicFactor = 0.0
+#         pbr_material.roughnessFactor = 0.9
+
+#     if normal_texture is not None:
+#         gltf.model.materials[0].normalTexture = NormalTextureInfo(
+#             index=add_texture(gltf, normal_texture), scale=normal_strength
+#         )
+
+#     gltf.model.nodes[0].name = f"mvadapter_node_{task_id}"
+#     gltf.model.meshes[0].name = f"mvadapter_mesh_{task_id}"
+#     gltf.model.materials[0].name = f"mvadapter_material_{task_id}"
+#     gltf.model.asset.generator = "https://github.com/huanngzh/MV-Adapter"
+#     gltf.export(output_path)
+
+
+def replace_mesh_texture_and_save(
+    input_path: str,
+    output_path: str,
+    texture: Union[Image.Image, np.ndarray, torch.Tensor],
+    metallic_roughness_texture: Optional[
+        Union[Image.Image, np.ndarray, torch.Tensor]
+    ] = None,
+    normal_texture: Optional[Union[Image.Image, np.ndarray, torch.Tensor]] = None,
+    normal_strength: float = 1.0,
+    texture_format: str = "JPEG",
+    task_id: str = "",
+    backend: str = "trimesh",
+) -> None:
+    if backend == "trimesh":
+        replace_mesh_texture_and_save_trimesh(
+            input_path,
+            output_path,
+            texture,
+            metallic_roughness_texture=metallic_roughness_texture,
+            normal_texture=normal_texture,
+            texture_format=texture_format,
+            task_id=task_id,
+        )
+    # elif backend == "gltflib":
+    #     replace_mesh_texture_and_save_gltflib(
+    #         input_path,
+    #         output_path,
+    #         texture,
+    #         metallic_roughness_texture=metallic_roughness_texture,
+    #         normal_texture=normal_texture,
+    #         normal_strength=normal_strength,
+    #         task_id=task_id,
+    #     )
+    else:
+        raise NotImplementedError
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/mesh_process.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/mesh_process.py
new file mode 100644
index 0000000..37916fd
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/mesh_process.py
@@ -0,0 +1,348 @@
+import numpy as np
+import open3d as o3d
+import pymeshlab
+import torch
+import trimesh
+from pymeshlab import PercentageValue
+
+
+### Mesh Utils ###
+##### read mesh
+def read_mesh_from_path(mesh_path):
+    ms = pymeshlab.MeshSet()
+    ms.load_new_mesh(mesh_path)
+    return ms
+
+
+def mesh_to_meshlab(vertices, faces):
+    mesh = pymeshlab.Mesh(vertex_matrix=vertices, face_matrix=faces)
+    ms = pymeshlab.MeshSet()
+    ms.add_mesh(mesh)
+    return ms
+
+
+def meshlab_to_mesh(ms):
+    m = ms.current_mesh()
+    return m.vertex_matrix(), m.face_matrix(), m.vertex_normal_matrix()
+
+
+##### decimation
+def decimate_quadric_edge_collapse_with_texture(
+    ms, targetfacenum=None, preservenormal=True, verbose=False
+):
+    # targetfacenum: int, Target number of faces.
+    # preservenormal: bool, Preserve the normals of the original mesh.
+    if verbose:
+        print("Starting decimation ...  ")
+    m = ms.current_mesh()
+    if targetfacenum is None:
+        targetfacenum = int(m.face_number() * 0.5)
+    if verbose:
+        print("... Initial face number is %d ... " % m.face_number())
+    ms.meshing_decimation_quadric_edge_collapse_with_texture(
+        targetfacenum=targetfacenum, preservenormal=preservenormal
+    )
+    if verbose:
+        print("... Decimated face number is %d ... " % m.face_number())
+        print("Decimation done!\n ")
+
+
+def decimate_quadric_edge_collapse(
+    ms, targetfacenum=None, preservenormal=True, verbose=False
+):
+    # targetfacenum: int, Target number of faces.
+    # preservenormal: bool, Preserve the normals of the original mesh.
+    if verbose:
+        print("Starting decimation ...  ")
+    m = ms.current_mesh()
+    if targetfacenum is None:
+        targetfacenum = int(m.face_number() * 0.5)
+    if verbose:
+        print("... Initial face number is %d ... " % m.face_number())
+    ms.meshing_decimation_quadric_edge_collapse(
+        targetfacenum=targetfacenum, preservenormal=preservenormal
+    )
+    if verbose:
+        print("... Decimated face number is %d ... " % m.face_number())
+        print("Decimation done!\n ")
+
+
+##### vertex merge
+def merge_close_vertices(ms, threshold=0.0001, verbose=False):
+    # threshold: float, Merge together all the vertices that are nearer than the specified threshold.
+    if verbose:
+        print("Starting merge vertices ...  ")
+    m = ms.current_mesh()
+    if verbose:
+        print("... Initial vertex number is %d ... " % m.vertex_number())
+    ms.meshing_merge_close_vertices(threshold=PercentageValue(threshold * 100))
+    if verbose:
+        print("... Merged vertex number is %d ... " % m.vertex_number())
+        print("Merge vertices done!\n ")
+
+
+##### Island Removal
+def remove_isolated_pieces(ms, mincomponentsize=25, diameter=None, verbose=False):
+    # mincomponentsize: Delete isolated connected components composed by a limited number of triangles
+    # diameter: Delete isolated connected components whose diameter is smaller than the specified constant
+    if verbose:
+        print("Starting remove isolated pieces ...  ")
+    m = ms.current_mesh()
+    if verbose:
+        print("... Initial face number is %d ... " % m.face_number())
+    if diameter is None:
+        ms.meshing_remove_connected_component_by_face_number(
+            mincomponentsize=mincomponentsize, removeunref=True
+        )
+    else:
+        ms.meshing_remove_connected_component_by_diameter(
+            mincomponentdiag=PercentageValue(diameter), removeunref=True
+        )
+    if verbose:
+        print("... Isolated removed face number is %d ... " % m.face_number())
+        print("Remove isolated pieces done!\n ")
+
+
+##### hole filling
+def fix_hole(ms, maxholesize=30, verbose=False):
+    # maxholesize: int, Maximum size of the hole to be filled.
+    if verbose:
+        print("Starting fix holes ...  ")
+    m = ms.current_mesh()
+    if verbose:
+        print("... Initial face number is %d ... " % m.face_number())
+    ms.meshing_close_holes(maxholesize=maxholesize)
+    if verbose:
+        print("... Fixed hole face number is %d ... " % m.face_number())
+        print("Fix holes done!\n ")
+
+
+##### repair non manifold edges
+def repair_non_manifold(ms, verbose=False):
+    if verbose:
+        print("Starting repair non manifold edges ...  ")
+    m = ms.current_mesh()
+    if verbose:
+        print("... Initial face number is %d ... " % m.face_number())
+    ms.meshing_repair_non_manifold_edges()
+    ms.meshing_repair_non_manifold_vertices(vertdispratio=0.1)
+    ms.meshing_remove_duplicate_faces()
+    if verbose:
+        print("... Fixed non manifold edges face number is %d ... " % m.face_number())
+        print("Repair non manifold edges done!\n ")
+
+
+##### laplacian_smooth
+def laplacian_smooth(ms, stepsmoothnum=3, verbose=False):
+    # stepsmoothnum: int, Number of smoothing steps to be performed
+    if verbose:
+        print("Starting laplacian smooth ...  ")
+    m = ms.current_mesh()
+    ms.apply_coord_laplacian_smoothing(stepsmoothnum=stepsmoothnum)
+    if verbose:
+        print("Laplacian smooth done!\n ")
+
+
+##### taubin_smooth
+def taubin_smooth(ms, stepsmoothnum=3, verbose=False):
+    if verbose:
+        print("Starting Taubin smooth ...  ")
+    m = ms.current_mesh()
+    ms.apply_coord_taubin_smoothing(stepsmoothnum=stepsmoothnum)
+    if verbose:
+        print("Taubin smooth done!\n ")
+
+
+##### compute_normal
+def compute_normal(ms, weightmode="Simple Average", verbose=False):
+    if verbose:
+        print("Starting compute_normal_per_vertex ...  ")
+    m = ms.current_mesh()
+    ms.compute_normal_per_vertex(weightmode=weightmode)
+    if verbose:
+        print("compute_normal_per_vertex done!\n ")
+
+
+### Pre-process Mesh ###
+def process_mesh(
+    vertices,
+    faces,
+    threshold=0.0001,
+    mincomponentRatio=0.02,
+    targetfacenum=50000,
+    maxholesize=30,
+    stepsmoothnum=10,
+    verbose=False,
+):
+    ms = mesh_to_meshlab(vertices, faces)
+
+    ### Vertex Merge
+    merge_close_vertices(ms, threshold=threshold, verbose=verbose)
+
+    ### Island Removal
+    faces = ms.current_mesh().face_matrix()
+    remove_isolated_pieces(
+        ms, mincomponentsize=int(len(faces) * mincomponentRatio), verbose=verbose
+    )
+
+    ### Hole Filling
+    repair_non_manifold(ms)  # repair before fix hole
+    fix_hole(ms, maxholesize=maxholesize, verbose=verbose)
+
+    ### Taubin Smoothing
+    taubin_smooth(ms, stepsmoothnum=stepsmoothnum, verbose=verbose)
+
+    vertices, faces, _ = meshlab_to_mesh(ms)
+    if faces.shape[0] > targetfacenum:
+        device = o3d.core.Device("CPU:0")
+        dtype_f = o3d.core.float32
+        dtype_i = o3d.core.int64
+        mesh = o3d.t.geometry.TriangleMesh(device)
+        mesh.vertex.positions = o3d.core.Tensor(
+            vertices.astype(np.float32), dtype_f, device
+        )
+        mesh.triangle.indices = o3d.core.Tensor(faces.astype(np.int64), dtype_i, device)
+        simplified_mesh = mesh.simplify_quadric_decimation(
+            target_reduction=1.0 - float(targetfacenum) / faces.shape[0]
+        )
+        ms.clear()
+        vertices = simplified_mesh.vertex.positions.numpy()
+        faces = simplified_mesh.triangle.indices.numpy()
+        mesh = pymeshlab.Mesh(vertex_matrix=vertices, face_matrix=faces)
+        ms.add_mesh(mesh)
+
+    ### Mesh Simplification/Decimation
+    # decimate_quadric_edge_collapse(ms, targetfacenum=targetfacenum, verbose=verbose)
+    taubin_smooth(ms, stepsmoothnum=stepsmoothnum, verbose=verbose)
+    repair_non_manifold(ms, verbose=verbose)
+    compute_normal(ms, verbose=verbose)
+    return meshlab_to_mesh(ms)
+
+
+### UV Un-Warp ###
+def uv_parameterize_uvatlas(
+    vertices,
+    faces,
+    size=1024,
+    gutter=2.5,
+    max_stretch=0.1666666716337204,
+    parallel_partitions=16,
+    nthreads=0,
+):
+    device = o3d.core.Device("CPU:0")
+    dtype_f = o3d.core.float32
+    dtype_i = o3d.core.int64
+
+    mesh = o3d.t.geometry.TriangleMesh(device)
+
+    mesh.vertex.positions = o3d.core.Tensor(
+        vertices.astype(np.float32), dtype_f, device
+    )
+    mesh.triangle.indices = o3d.core.Tensor(faces.astype(np.int64), dtype_i, device)
+
+    mesh.compute_uvatlas(
+        size=size,
+        gutter=gutter,
+        max_stretch=max_stretch,
+        parallel_partitions=parallel_partitions,
+        nthreads=nthreads,
+    )
+
+    return mesh.triangle.texture_uvs.numpy()  # (#F, 3, 2)
+
+
+### Pack All ###
+def process_raw(mesh_path, save_path, preprocess=True, device="cpu"):
+    scene = trimesh.load(mesh_path, force="mesh", process=False)
+    if isinstance(scene, trimesh.Trimesh):
+        mesh = scene
+    elif isinstance(scene, trimesh.scene.Scene):
+        mesh = trimesh.Trimesh()
+        for obj in scene.geometry.values():
+            mesh = trimesh.util.concatenate([mesh, obj])
+    else:
+        raise ValueError(f"Unknown mesh type at {mesh_path}.")
+
+    vertices = mesh.vertices
+    faces = mesh.faces
+
+    mesh_post_process_options = {
+        "mincomponentRatio": 0.02,
+        "targetfacenum": 50000,
+        "maxholesize": 100,
+        "stepsmoothnum": 10,
+        "verbose": False,
+    }
+
+    if preprocess:
+        v_pos, t_pos_idx, normals = process_mesh(
+            vertices=vertices,
+            faces=faces,
+            **mesh_post_process_options,
+        )
+    else:
+        v_pos, t_pos_idx, normals = vertices, faces, mesh.vertex_normals
+
+    v_tex_np = (
+        uv_parameterize_uvatlas(v_pos, t_pos_idx).reshape(-1, 2).astype(np.float32)
+    )
+
+    v_pos = torch.from_numpy(v_pos).to(device=device, dtype=torch.float32)
+    t_pos_idx = torch.from_numpy(t_pos_idx).to(device=device, dtype=torch.long)
+    v_tex = torch.from_numpy(v_tex_np).to(device=device, dtype=torch.float32)
+    normals = torch.from_numpy(normals).to(device=device, dtype=torch.float32)
+    assert v_tex.shape[0] == t_pos_idx.shape[0] * 3
+    t_tex_idx = torch.arange(
+        t_pos_idx.shape[0] * 3,
+        device=device,
+        dtype=torch.long,
+    ).reshape(-1, 3)
+    # uv, index = torch.unique(v_tex, dim=0, return_inverse=True) # 这样实现是2毫秒
+    # super efficient de-duplication
+    v_tex_u_uint32 = v_tex_np[..., 0].view(np.uint32)
+    v_tex_v_uint32 = v_tex_np[..., 1].view(np.uint32)
+    v_hashed = (v_tex_u_uint32.astype(np.uint64) << 32) | v_tex_v_uint32
+    v_hashed = torch.from_numpy(v_hashed.view(np.int64)).to(v_pos.device)
+
+    t_pos_idx_f3 = torch.arange(
+        t_pos_idx.shape[0] * 3, device=t_pos_idx.device, dtype=torch.long
+    ).reshape(-1, 3)
+    v_pos_f3 = v_pos[t_pos_idx].reshape(-1, 3)
+    normals_f3 = normals[t_pos_idx].reshape(-1, 3)
+
+    v_hashed_dedup, inverse_indices = torch.unique(v_hashed, return_inverse=True)
+    dedup_size, full_size = v_hashed_dedup.shape[0], inverse_indices.shape[0]
+    indices = torch.scatter_reduce(
+        torch.full(
+            [dedup_size],
+            fill_value=full_size,
+            device=inverse_indices.device,
+            dtype=torch.long,
+        ),
+        index=inverse_indices,
+        src=torch.arange(full_size, device=inverse_indices.device, dtype=torch.int64),
+        dim=0,
+        reduce="amin",
+    )
+    v_tex = v_tex[indices]
+    t_tex_idx = inverse_indices.reshape(-1, 3)
+
+    v_pos = v_pos_f3[indices]
+    normals = normals_f3[indices]
+
+    normals = normals.to(dtype=torch.float32, device=device)
+
+    # either flip uv or flip texture
+    # here we flip uv
+    uv_to_save = v_tex.clone()
+    uv_to_save[:, 1] = 1.0 - uv_to_save[:, 1]
+
+    visual = trimesh.visual.TextureVisuals(uv=uv_to_save.cpu().numpy())
+    tmesh = trimesh.Trimesh(
+        vertices=v_pos.cpu().numpy(),
+        faces=t_tex_idx.cpu().numpy(),
+        vertex_normals=normals.cpu().numpy(),
+        visual=visual,
+        process=False,
+    )
+    tmesh.export(save_path)
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/projection.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/projection.py
new file mode 100755
index 0000000..0409b66
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/projection.py
@@ -0,0 +1,204 @@
+import glob
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+from einops import rearrange
+from PIL import Image
+
+from .blend import PoissonBlendingSolver
+from .camera import Camera, get_camera, get_orthogonal_camera
+from .mesh import TexturedMesh, load_mesh, replace_mesh_texture_and_save
+from .render import NVDiffRastContextWrapper, SimpleNormalization, render
+from .seg import SegmentationModel
+from .utils import (
+    IMAGE_TYPE,
+    LIST_TYPE,
+    image_to_tensor,
+    make_image_grid,
+    tensor_to_image,
+)
+from .uv import (
+    ExponentialBlend,
+    SimpleUVValidityStrategy,
+    uv_blend,
+    uv_precompute,
+    uv_render_attr,
+    uv_render_geometry,
+)
+from .warp import compute_warp_field
+
+
+@dataclass
+class CameraProjectionOutput:
+    uv_proj: torch.FloatTensor
+    uv_proj_mask: torch.BoolTensor
+    uv_depth_grad: Optional[torch.FloatTensor]
+    uv_aoi_cos: Optional[torch.FloatTensor]
+
+
+class CameraProjection:
+    def __init__(
+        self,
+        pb_backend: str,
+        bg_remover: Optional[SegmentationModel],
+        device: str,
+        context_type: str = "gl",
+    ) -> None:
+        self.pb_solver = PoissonBlendingSolver(pb_backend, device)
+        self.ctx = NVDiffRastContextWrapper(device, context_type)
+        self.bg_remover = bg_remover
+        self.device = device
+
+    def __call__(
+        self,
+        images: IMAGE_TYPE,
+        mesh: TexturedMesh,
+        cam: Optional[Camera] = None,
+        fovy_deg: Optional[LIST_TYPE] = None,
+        masks: Optional[IMAGE_TYPE] = None,
+        remove_bg: bool = False,
+        c2w: Optional[torch.FloatTensor] = None,
+        elevation_deg: Optional[LIST_TYPE] = None,
+        distance: Optional[LIST_TYPE] = None,
+        azimuth_deg: Optional[LIST_TYPE] = None,
+        num_views: Optional[int] = None,
+        uv_size: int = 2048,
+        warp_images: bool = False,
+        images_background: Optional[float] = None,
+        iou_rejection_threshold: Optional[float] = 0.8,
+        aoi_cos_valid_threshold: float = 0.3,
+        depth_grad_dilation: int = 5,
+        depth_grad_threshold: float = 0.1,
+        uv_exp_blend_alpha: float = 6,
+        uv_exp_blend_view_weight: Optional[torch.FloatTensor] = None,
+        poisson_blending: bool = True,
+        pb_num_iters: int = 1000,
+        pb_keep_original_border: bool = True,
+        from_scratch: bool = False,
+        uv_padding: bool = True,
+        return_uv_projection_mask: bool = False,
+        return_dict: bool = False,
+    ) -> Optional[torch.FloatTensor]:
+        images_pt = image_to_tensor(images, device=self.device)
+        assert images_pt.ndim == 4
+        Nv, H, W, _ = images_pt.shape
+
+        if masks is not None:
+            masks_pt = image_to_tensor(masks, device=self.device)
+        else:
+            if remove_bg:
+                assert self.bg_remover is not None
+                masks_pt = self.bg_remover(images_pt)
+            else:
+                masks_pt = None
+
+        if masks_pt is not None and masks_pt.ndim == 4:
+            masks_pt = masks_pt.mean(-1)
+
+        if cam is None:
+            cam = get_camera(
+                elevation_deg,
+                distance,
+                fovy_deg,
+                azimuth_deg,
+                num_views,
+                c2w,
+                aspect_wh=W / H,
+                device=self.device,
+            )
+        uv_precompute_output = uv_precompute(
+            self.ctx, mesh, height=uv_size, width=uv_size
+        )
+        uv_render_geometry_output = uv_render_geometry(
+            self.ctx,
+            mesh,
+            cam,
+            view_height=H,
+            view_width=W,
+            uv_precompute_output=uv_precompute_output,
+            compute_depth_grad=True,
+            depth_grad_dilation=depth_grad_dilation,
+        )
+
+        # IoU rejection
+        if masks_pt is not None and iou_rejection_threshold is not None:
+            given_masks = (masks_pt > 0.5).float()
+            render_masks = uv_render_geometry_output.view_mask.float()
+            intersection = given_masks * render_masks
+            union = given_masks + render_masks - intersection
+            iou = intersection.sum((1, 2)) / union.sum((1, 2))
+            iou_min = iou.min()
+            print(f"Debug: Per view IoU: {iou.tolist()}")
+            if iou_min < iou_rejection_threshold:
+                print(
+                    f"Warning: Minimum view IoU {iou_min} below threshold {iou_rejection_threshold}, skipping camera projection!"
+                )
+                return None
+
+        if warp_images:
+            # TODO: clean code
+            assert images_background is not None
+            render_attr = render(
+                self.ctx,
+                mesh,
+                cam,
+                height=H,
+                width=W,
+                render_attr=True,
+                attr_background=images_background,
+            ).attr
+            images_pt = compute_warp_field(
+                self.ctx.ctx,
+                images_pt,
+                render_attr,
+                n_grid=10,
+                optim_res=[64, 128],
+                optim_step_per_res=20,
+                lambda_reg=2.0,
+                temp_dir="debug_warp",
+                verbose=False,
+                device=self.device,
+            )
+
+        uv_render_attr_output = uv_render_attr(
+            images=images_pt,
+            masks=masks_pt,
+            uv_render_geometry_output=uv_render_geometry_output,
+        )
+        uv_blend_output = uv_blend(
+            uv_precompute_output,
+            uv_render_geometry_output,
+            uv_render_attr_output,
+            uv_validity_strategy=SimpleUVValidityStrategy(
+                aoi_cos_thresh=aoi_cos_valid_threshold,
+                depth_grad_thresh=depth_grad_threshold,
+            ),
+            uv_blend_weight_strategy=ExponentialBlend(
+                alpha=uv_exp_blend_alpha, view_weight=uv_exp_blend_view_weight
+            ),
+            empty_value=1.0,
+            do_uv_padding=uv_padding,
+            pad_unseen_area=from_scratch,
+            poisson_blending=poisson_blending,
+            pb_solver=self.pb_solver,
+            pb_num_iters=pb_num_iters,
+            pb_keep_original_border=pb_keep_original_border,
+        )
+
+        if return_dict:
+            # recommonded new way to get return value
+            return CameraProjectionOutput(
+                uv_proj=uv_blend_output.uv_attr_blend,
+                uv_proj_mask=uv_blend_output.uv_valid_mask_blend,
+                uv_depth_grad=uv_render_geometry_output.uv_depth_grad,
+                uv_aoi_cos=uv_render_geometry_output.uv_aoi_cos,
+            )
+        else:
+            if return_uv_projection_mask:
+                return (
+                    uv_blend_output.uv_attr_blend,
+                    uv_blend_output.uv_valid_mask_blend,
+                )
+            return uv_blend_output.uv_attr_blend
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/render.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/render.py
new file mode 100644
index 0000000..704f2d3
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/render.py
@@ -0,0 +1,310 @@
+import math
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List, Optional, Union
+
+import numpy as np
+import nvdiffrast.torch as dr
+import torch
+import torch.nn.functional as F
+import trimesh
+from PIL import Image
+from torch import BoolTensor, FloatTensor
+
+from .camera import Camera
+from .mesh import TexturedMesh
+from .utils import get_clip_space_position, transform_points_homo
+
+
+@dataclass
+class RenderOutput:
+    attr: Optional[torch.FloatTensor] = None
+    mask: Optional[torch.BoolTensor] = None
+    depth: Optional[torch.FloatTensor] = None
+    normal: Optional[torch.FloatTensor] = None
+    tangent: Optional[torch.FloatTensor] = None
+    pos: Optional[torch.FloatTensor] = None
+
+
+class NVDiffRastContextWrapper:
+    def __init__(self, device: str, context_type: str = "gl"):
+        if context_type == "gl":
+            self.ctx = dr.RasterizeCudaContext(device=device)
+        elif context_type == "cuda":
+            self.ctx = dr.RasterizeCudaContext(device=device)
+        else:
+            raise NotImplementedError
+
+    def rasterize(self, pos, tri, resolution, ranges=None, grad_db=True):
+        """
+        Rasterize triangles.
+
+        All input tensors must be contiguous and reside in GPU memory except for the ranges tensor that, if specified, has to reside in CPU memory. The output tensors will be contiguous and reside in GPU memory.
+
+        Arguments:
+        glctx	Rasterizer context of type RasterizeGLContext or RasterizeCudaContext.
+        pos	Vertex position tensor with dtype torch.float32. To enable range mode, this tensor should have a 2D shape [num_vertices, 4]. To enable instanced mode, use a 3D shape [minibatch_size, num_vertices, 4].
+        tri	Triangle tensor with shape [num_triangles, 3] and dtype torch.int32.
+        resolution	Output resolution as integer tuple (height, width).
+        ranges	In range mode, tensor with shape [minibatch_size, 2] and dtype torch.int32, specifying start indices and counts into tri. Ignored in instanced mode.
+        grad_db	Propagate gradients of image-space derivatives of barycentrics into pos in backward pass. Ignored if using an OpenGL context that was not configured to output image-space derivatives.
+        Returns:
+        A tuple of two tensors. The first output tensor has shape [minibatch_size, height, width, 4] and contains the main rasterizer output in order (u, v, z/w, triangle_id). If the OpenGL context was configured to output image-space derivatives of barycentrics, the second output tensor will also have shape [minibatch_size, height, width, 4] and contain said derivatives in order (du/dX, du/dY, dv/dX, dv/dY). Otherwise it will be an empty tensor with shape [minibatch_size, height, width, 0].
+        """
+        return dr.rasterize(
+            self.ctx,
+            pos.float().contiguous(),
+            tri.int().contiguous(),
+            resolution,
+            ranges,
+            grad_db,
+        )
+
+    def interpolate(self, attr, rast, tri, rast_db=None, diff_attrs=None):
+        """
+        Interpolate vertex attributes.
+
+        All input tensors must be contiguous and reside in GPU memory. The output tensors will be contiguous and reside in GPU memory.
+
+        Arguments:
+        attr	Attribute tensor with dtype torch.float32. Shape is [num_vertices, num_attributes] in range mode, or [minibatch_size, num_vertices, num_attributes] in instanced mode. Broadcasting is supported along the minibatch axis.
+        rast	Main output tensor from rasterize().
+        tri	Triangle tensor with shape [num_triangles, 3] and dtype torch.int32.
+        rast_db	(Optional) Tensor containing image-space derivatives of barycentrics, i.e., the second output tensor from rasterize(). Enables computing image-space derivatives of attributes.
+        diff_attrs	(Optional) List of attribute indices for which image-space derivatives are to be computed. Special value 'all' is equivalent to list [0, 1, ..., num_attributes - 1].
+        Returns:
+        A tuple of two tensors. The first output tensor contains interpolated attributes and has shape [minibatch_size, height, width, num_attributes]. If rast_db and diff_attrs were specified, the second output tensor contains the image-space derivatives of the selected attributes and has shape [minibatch_size, height, width, 2 * len(diff_attrs)]. The derivatives of the first selected attribute A will be on channels 0 and 1 as (dA/dX, dA/dY), etc. Otherwise, the second output tensor will be an empty tensor with shape [minibatch_size, height, width, 0].
+        """
+        return dr.interpolate(
+            attr.float().contiguous(), rast, tri.int().contiguous(), rast_db, diff_attrs
+        )
+
+    def texture(
+        self,
+        tex,
+        uv,
+        uv_da=None,
+        mip_level_bias=None,
+        mip=None,
+        filter_mode="auto",
+        boundary_mode="wrap",
+        max_mip_level=None,
+    ):
+        """
+        Perform texture sampling.
+
+        All input tensors must be contiguous and reside in GPU memory. The output tensor will be contiguous and reside in GPU memory.
+
+        Arguments:
+        tex	Texture tensor with dtype torch.float32. For 2D textures, must have shape [minibatch_size, tex_height, tex_width, tex_channels]. For cube map textures, must have shape [minibatch_size, 6, tex_height, tex_width, tex_channels] where tex_width and tex_height are equal. Note that boundary_mode must also be set to 'cube' to enable cube map mode. Broadcasting is supported along the minibatch axis.
+        uv	Tensor containing per-pixel texture coordinates. When sampling a 2D texture, must have shape [minibatch_size, height, width, 2]. When sampling a cube map texture, must have shape [minibatch_size, height, width, 3].
+        uv_da	(Optional) Tensor containing image-space derivatives of texture coordinates. Must have same shape as uv except for the last dimension that is to be twice as long.
+        mip_level_bias	(Optional) Per-pixel bias for mip level selection. If uv_da is omitted, determines mip level directly. Must have shape [minibatch_size, height, width].
+        mip	(Optional) Preconstructed mipmap stack from a texture_construct_mip() call, or a list of tensors specifying a custom mipmap stack. When specifying a custom mipmap stack, the tensors in the list must follow the same format as tex except for width and height that must follow the usual rules for mipmap sizes. The base level texture is still supplied in tex and must not be included in the list. Gradients of a custom mipmap stack are not automatically propagated to base texture but the mipmap tensors will receive gradients of their own. If a mipmap stack is not specified but the chosen filter mode requires it, the mipmap stack is constructed internally and discarded afterwards.
+        filter_mode	Texture filtering mode to be used. Valid values are 'auto', 'nearest', 'linear', 'linear-mipmap-nearest', and 'linear-mipmap-linear'. Mode 'auto' selects 'linear' if neither uv_da or mip_level_bias is specified, and 'linear-mipmap-linear' when at least one of them is specified, these being the highest-quality modes possible depending on the availability of the image-space derivatives of the texture coordinates or direct mip level information.
+        boundary_mode	Valid values are 'wrap', 'clamp', 'zero', and 'cube'. If tex defines a cube map, this must be set to 'cube'. The default mode 'wrap' takes fractional part of texture coordinates. Mode 'clamp' clamps texture coordinates to the centers of the boundary texels. Mode 'zero' virtually extends the texture with all-zero values in all directions.
+        max_mip_level	If specified, limits the number of mipmaps constructed and used in mipmap-based filter modes.
+        Returns:
+        A tensor containing the results of the texture sampling with shape [minibatch_size, height, width, tex_channels]. Cube map fetches with invalid uv coordinates (e.g., zero vectors) output all zeros and do not propagate gradients.
+        """
+        return dr.texture(
+            tex.float(),
+            uv.float(),
+            uv_da,
+            mip_level_bias,
+            mip,
+            filter_mode,
+            boundary_mode,
+            max_mip_level,
+        )
+
+    def antialias(
+        self, color, rast, pos, tri, topology_hash=None, pos_gradient_boost=1.0
+    ):
+        """
+        Perform antialiasing.
+
+        All input tensors must be contiguous and reside in GPU memory. The output tensor will be contiguous and reside in GPU memory.
+
+        Note that silhouette edge determination is based on vertex indices in the triangle tensor. For it to work properly, a vertex belonging to multiple triangles must be referred to using the same vertex index in each triangle. Otherwise, nvdiffrast will always classify the adjacent edges as silhouette edges, which leads to bad performance and potentially incorrect gradients. If you are unsure whether your data is good, check which pixels are modified by the antialias operation and compare to the example in the documentation.
+
+        Arguments:
+        color	Input image to antialias with shape [minibatch_size, height, width, num_channels].
+        rast	Main output tensor from rasterize().
+        pos	Vertex position tensor used in the rasterization operation.
+        tri	Triangle tensor used in the rasterization operation.
+        topology_hash	(Optional) Preconstructed topology hash for the triangle tensor. If not specified, the topology hash is constructed internally and discarded afterwards.
+        pos_gradient_boost	(Optional) Multiplier for gradients propagated to pos.
+        Returns:
+        A tensor containing the antialiased image with the same shape as color input tensor.
+        """
+        return dr.antialias(
+            color.float(),
+            rast,
+            pos.float(),
+            tri.int(),
+            topology_hash,
+            pos_gradient_boost,
+        )
+
+
+class DepthNormalizationStrategy(ABC):
+    @abstractmethod
+    def __init__(self, *args, **kwargs):
+        pass
+
+    @abstractmethod
+    def __call__(
+        self, depth: torch.FloatTensor, mask: torch.BoolTensor
+    ) -> torch.FloatTensor:
+        pass
+
+
+class DepthControlNetNormalization(DepthNormalizationStrategy):
+    def __init__(
+        self, far_clip: float = 0.25, near_clip: float = 1.0, bg_value: float = 0.0
+    ):
+        self.far_clip = far_clip
+        self.near_clip = near_clip
+        self.bg_value = bg_value
+
+    def __call__(
+        self, depth: torch.FloatTensor, mask: torch.BoolTensor
+    ) -> torch.FloatTensor:
+        batch_size = depth.shape[0]
+        min_depth = depth.view(batch_size, -1).min(dim=-1)[0][:, None, None]
+        max_depth = depth.view(batch_size, -1).max(dim=-1)[0][:, None, None]
+        depth = 1.0 - ((depth - min_depth) / (max_depth - min_depth + 1e-5)).clamp(
+            0.0, 1.0
+        )
+        depth = depth * (self.near_clip - self.far_clip) + self.far_clip
+        depth[~mask] = self.bg_value
+        return depth
+
+
+class Zero123PlusPlusNormalization(DepthNormalizationStrategy):
+    def __init__(self, bg_value: float = 0.8):
+        self.bg_value = bg_value
+
+    def __call__(self, depth: FloatTensor, mask: BoolTensor) -> FloatTensor:
+        batch_size = depth.shape[0]
+        min_depth = depth.view(batch_size, -1).min(dim=-1)[0][:, None, None]
+        max_depth = depth.view(batch_size, -1).max(dim=-1)[0][:, None, None]
+        depth = ((depth - min_depth) / (max_depth - min_depth + 1e-5)).clamp(0.0, 1.0)
+        depth[~mask] = self.bg_value
+        return depth
+
+
+class SimpleNormalization(DepthNormalizationStrategy):
+    def __init__(
+        self,
+        scale: float = 1.0,
+        offset: float = -1.0,
+        clamp: bool = True,
+        bg_value: float = 1.0,
+    ):
+        self.scale = scale
+        self.offset = offset
+        self.clamp = clamp
+        self.bg_value = bg_value
+
+    def __call__(self, depth: FloatTensor, mask: BoolTensor) -> FloatTensor:
+        depth = depth * self.scale + self.offset
+        if self.clamp:
+            depth = depth.clamp(0.0, 1.0)
+        depth[~mask] = self.bg_value
+        return depth
+
+
+def render(
+    ctx: NVDiffRastContextWrapper,
+    mesh: TexturedMesh,
+    cam: Camera,
+    height: int,
+    width: int,
+    render_attr: bool = True,
+    render_depth: bool = True,
+    render_normal: bool = True,
+    render_tangent: bool = False,
+    depth_normalization_strategy: DepthNormalizationStrategy = DepthControlNetNormalization(),
+    attr_background: Union[float, torch.FloatTensor] = 0.5,
+    antialias_attr=False,
+    normal_background: Union[float, torch.FloatTensor] = 0.0,
+    tangent_background: Union[float, torch.FloatTensor] = 0.0,
+    texture_override=None,
+    texture_filter_mode: str = "linear",
+) -> RenderOutput:
+    output_dict = {}
+
+    v_pos_clip = get_clip_space_position(mesh.v_pos, cam.mvp_mtx)
+    rast, _ = ctx.rasterize(v_pos_clip, mesh.t_pos_idx, (height, width), grad_db=True)
+    mask = rast[..., 3] > 0
+
+    gb_pos, _ = ctx.interpolate(mesh.v_pos[None], rast, mesh.t_pos_idx)
+    output_dict.update({"mask": mask, "pos": gb_pos})
+
+    if render_depth:
+        gb_pos_vs = transform_points_homo(gb_pos, cam.w2c)
+        gb_depth = -gb_pos_vs[..., 2]
+        # set background pixels to min depth value for correct min/max calculation
+        gb_depth = torch.where(
+            mask,
+            gb_depth,
+            gb_depth.view(gb_depth.shape[0], -1).min(dim=-1)[0][:, None, None],
+        )
+        gb_depth = depth_normalization_strategy(gb_depth, mask)
+        output_dict["depth"] = gb_depth
+
+    if render_attr:
+        tex_c, _ = ctx.interpolate(mesh.v_tex[None], rast, mesh.t_tex_idx)
+        texture = (
+            texture_override[None]
+            if texture_override is not None
+            else mesh.texture[None]
+        )
+        gb_rgb_fg = ctx.texture(texture, tex_c, filter_mode=texture_filter_mode)
+        gb_rgb_bg = torch.ones_like(gb_rgb_fg) * attr_background
+        gb_rgb = torch.where(mask[..., None], gb_rgb_fg, gb_rgb_bg)
+        if antialias_attr:
+            gb_rgb = ctx.antialias(gb_rgb, rast, v_pos_clip, mesh.t_pos_idx)
+        output_dict["attr"] = gb_rgb
+
+    if render_normal:
+        gb_nrm, _ = ctx.interpolate(mesh.v_nrm[None], rast, mesh.stitched_t_pos_idx)
+        gb_nrm = F.normalize(gb_nrm, dim=-1, p=2)
+        gb_nrm[~mask] = normal_background
+        output_dict["normal"] = gb_nrm
+
+    if render_tangent:
+        gb_tang, _ = ctx.interpolate(mesh.v_tang[None], rast, mesh.stitched_t_pos_idx)
+        gb_tang = F.normalize(gb_tang, dim=-1, p=2)
+        gb_tang[~mask] = tangent_background
+        output_dict["tangent"] = gb_tang
+
+    return RenderOutput(**output_dict)
+
+
+def tensor_to_image(
+    data: Union[Image.Image, torch.Tensor, np.ndarray],
+    batched: bool = False,
+    format: str = "HWC",
+) -> Union[Image.Image, List[Image.Image]]:
+    if isinstance(data, Image.Image):
+        return data
+    if isinstance(data, torch.Tensor):
+        data = data.detach().cpu().numpy()
+    if data.dtype == np.float32 or data.dtype == np.float16:
+        data = (data * 255).astype(np.uint8)
+    elif data.dtype == np.bool_:
+        data = data.astype(np.uint8) * 255
+    assert data.dtype == np.uint8
+    if format == "CHW":
+        if batched and data.ndim == 4:
+            data = data.transpose((0, 2, 3, 1))
+        elif not batched and data.ndim == 3:
+            data = data.transpose((1, 2, 0))
+
+    if batched:
+        return [Image.fromarray(d) for d in data]
+    return Image.fromarray(data)
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/seg.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/seg.py
new file mode 100755
index 0000000..f74c67e
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/seg.py
@@ -0,0 +1,38 @@
+from abc import ABC, abstractmethod
+
+import torch
+import transformers
+
+from .utils import IMAGE_TYPE, image_to_tensor
+
+
+class SegmentationModel(ABC):
+    def __init__(self, *args, **kwargs):
+        pass
+
+    def __call__(self, images: IMAGE_TYPE) -> torch.FloatTensor:
+        pass
+
+
+class RMBGModel(SegmentationModel):
+    def __init__(self, pretrained_model_name_or_path: str, device: str):
+        self.model = transformers.AutoModelForImageSegmentation.from_pretrained(
+            pretrained_model_name_or_path, trust_remote_code=True
+        ).to(device)
+        self.device = device
+
+    def __call__(self, images: IMAGE_TYPE) -> torch.FloatTensor:
+        images = image_to_tensor(images, device=self.device)
+        batched = True
+        if images.ndim == 3:
+            images = images.unsqueeze(0)
+            batched = False
+
+        out = (
+            self.model(images.permute(0, 3, 1, 2) - 0.5)[0][0]
+            .clamp(0.0, 1.0)
+            .permute(0, 2, 3, 1)
+        )
+        if not batched:
+            out = out.squeeze(0)
+        return out
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/smart_paint.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/smart_paint.py
new file mode 100644
index 0000000..21931ab
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/smart_paint.py
@@ -0,0 +1,334 @@
+import glob
+import os
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from itertools import product
+from typing import Any, Dict, List, Optional, Union
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+
+from .camera import Camera, get_camera
+from .mesh import (
+    TexturedMesh,
+    load_mesh,
+    mesh_use_texture,
+    replace_mesh_texture_and_save,
+)
+from .projection import CameraProjection
+from .render import NVDiffRastContextWrapper, render
+from .utils import image_to_tensor, make_image_grid, tensor_to_image
+from .uv import (
+    ExponentialBlend,
+    NVDiffRastContextWrapper,
+    PoissonBlendingSolver,
+    SimpleUVValidityStrategy,
+    uv_blend,
+    uv_padding,
+    uv_precompute,
+    uv_render_attr,
+    uv_render_geometry,
+)
+
+
+class SmartPainter:
+    def __init__(self, device: str):
+        self.device = device
+        self.cam_proj = CameraProjection(
+            pb_backend="torch-cuda", bg_remover=None, device=device
+        )
+        self.ctx = NVDiffRastContextWrapper(device=self.device)
+
+    def __call__(
+        self,
+        mod_name: str,
+        mesh: TexturedMesh,
+        inpaint_func: callable,
+        uv_texture: torch.FloatTensor,
+        uv_inpaint_mask: torch.BoolTensor,
+        max_view_score_thresh: float = 0.02,
+        min_rounds: int = 3,
+        max_rounds: int = 8,
+        uv_padding_end: bool = True,
+        debug_dir: Optional[str] = None,
+        debug_visualize_details: bool = False,
+    ):
+        def make_view_selection_cam_params():
+            elevation_anchors = range(-60, 61, 15)
+            azimuth_anchors = range(0, 360, 30)
+            distance_anchors = [1.2]
+            fovy_deg_anchors = [40]
+            params = list(
+                product(
+                    elevation_anchors,
+                    azimuth_anchors,
+                    distance_anchors,
+                    fovy_deg_anchors,
+                )
+            )
+            params = list(zip(*params))
+            return params
+
+        elevation_deg, azimuth_deg, distance, fovy_deg = (
+            make_view_selection_cam_params()
+        )
+        view_selection_cams = get_camera(
+            elevation_deg=list(elevation_deg),
+            azimuth_deg=list(azimuth_deg),
+            distance=list(distance),
+            fovy_deg=list(fovy_deg),
+            perturb_camera_position=0.1,
+            device=self.device,
+        )
+
+        texture_update = uv_texture.clone()
+        uv_valid_mask_update = ~uv_inpaint_mask.clone()
+        score_map_update = torch.zeros_like(uv_valid_mask_update, dtype=torch.float32)
+        score_map_update[uv_valid_mask_update] = 1.0
+
+        max_view_score = 1
+        i = 0
+        while i < min_rounds or (
+            max_view_score > max_view_score_thresh and i < max_rounds
+        ):
+            score_map_image = score_map_update.float()[:, :, None].repeat_interleave(
+                3, dim=-1
+            )
+            render_size = 256
+            with mesh_use_texture(mesh, score_map_image):
+                render_out = render(
+                    self.ctx,
+                    mesh,
+                    view_selection_cams,
+                    height=render_size,
+                    width=render_size,
+                    attr_background=1.0,
+                    texture_filter_mode="nearest",
+                )
+
+            def get_view_aoi_cos(render_out, cameras):
+                view_normal = render_out.normal
+                view_normal_cs = (
+                    view_normal[:, :, :, None, :] * cameras.w2c[:, None, None, :3, :3]
+                ).sum(-1)
+                view_normal_cs = F.normalize(view_normal_cs, dim=-1, p=2)
+                view_normal_cs[~render_out.mask] = 0
+                view_aoi_cos = (
+                    (
+                        view_normal_cs
+                        * torch.as_tensor(
+                            [0.0, 0.0, 1.0],
+                            dtype=view_normal_cs.dtype,
+                            device=view_normal_cs.device,
+                        )[None, None, None]
+                    )
+                    .sum(-1)
+                    .clamp(0, 1)
+                )
+                return view_aoi_cos
+
+            view_aoi_cos = get_view_aoi_cos(render_out, view_selection_cams)
+            if debug_dir is not None and debug_visualize_details:
+                make_image_grid(tensor_to_image(render_out.attr, batched=True)).save(
+                    os.path.join(debug_dir, f"{mod_name}_render_score_{i:02d}.jpg")
+                )
+                make_image_grid(tensor_to_image(view_aoi_cos, batched=True)).save(
+                    os.path.join(
+                        debug_dir, f"{mod_name}_render_view_aoi_cos_{i:02d}.jpg"
+                    )
+                )
+            view_score = [
+                (
+                    ((attr < 1e-3) & (aoi_cos > 0.1)).sum().item()
+                    + (
+                        ((attr > 1e-3) & (aoi_cos > 0.1)).float()
+                        * (aoi_cos - attr - 0.3).clamp_min(0)
+                    )
+                    .sum()
+                    .item()
+                )
+                / render_size**2
+                for attr, aoi_cos in zip(render_out.attr[..., 0], view_aoi_cos)
+            ]
+
+            max_view_score = np.max(view_score)
+            best_view = np.argmax(view_score)
+
+            inpaint_render_size = 1024
+            best_cam = view_selection_cams[best_view : best_view + 1]
+
+            def shrink_mask(mask: torch.BoolTensor, radius: int) -> torch.BoolTensor:
+                return (
+                    (
+                        -torch.nn.functional.max_pool2d(
+                            -(mask[None, None].float()),
+                            kernel_size=radius * 2 + 1,
+                            stride=1,
+                            padding=radius,
+                        )
+                    )
+                    .squeeze()
+                    .bool()
+                )
+
+            def enlarge_mask(mask: torch.BoolTensor, radius: int) -> torch.BoolTensor:
+                return (
+                    torch.nn.functional.max_pool2d(
+                        mask[None, None].float(),
+                        kernel_size=radius * 2 + 1,
+                        stride=1,
+                        padding=radius,
+                    )
+                    .squeeze()
+                    .bool()
+                )
+
+            def blur_mask(mask: torch.BoolTensor, radius: int) -> torch.FloatTensor:
+                # Create a Gaussian kernel
+                x = torch.arange(
+                    -radius, radius + 1, dtype=torch.float32, device=mask.device
+                )
+                kernel = torch.exp(-(x**2) / (2 * (radius / 2) ** 2))
+                kernel = kernel[None, None, None]
+                kernel = kernel * kernel.transpose(2, 3)
+                kernel = kernel / kernel.sum()
+                # Apply convolution to blur the mask
+                blurred_mask = F.conv2d(
+                    mask[None, None].float(), kernel, padding=radius
+                )
+                return blurred_mask.squeeze()
+
+            def get_occulusion_boundary(
+                view_depth: torch.FloatTensor, dilation: int, thresh: float
+            ) -> torch.BoolTensor:
+                grad_x_kernel = torch.tensor(
+                    [[1, 0, -1], [2, 0, -2], [1, 0, -1]],
+                    device=view_depth.device,
+                    dtype=torch.float32,
+                ).view(1, 1, 3, 3)
+                grad_y_kernel = torch.tensor(
+                    [[1, 2, 1], [0, 0, 0], [-1, -2, -1]],
+                    device=view_depth.device,
+                    dtype=torch.float32,
+                ).view(1, 1, 3, 3)
+                view_depth_grad_x = F.conv2d(
+                    view_depth[None, None], grad_x_kernel, padding=1
+                )
+                view_depth_grad_y = F.conv2d(
+                    view_depth[None, None], grad_y_kernel, padding=1
+                )
+                view_depth_grad = (view_depth_grad_x**2 + view_depth_grad_y**2).sqrt()
+                occ_boundary = (view_depth_grad > thresh)[0][0]
+                if dilation > 0:
+                    occ_boundary = enlarge_mask(occ_boundary, dilation)
+                return occ_boundary
+
+            with mesh_use_texture(mesh, score_map_image):
+                render_out = render(
+                    self.ctx,
+                    mesh,
+                    best_cam,
+                    height=inpaint_render_size,
+                    width=inpaint_render_size,
+                    attr_background=1.0,
+                    texture_filter_mode="nearest",
+                )
+
+            view_aoi_cos = get_view_aoi_cos(render_out, best_cam)
+            inpaint_mask = (render_out.attr[0, :, :, 0] < 1e-3) | (
+                view_aoi_cos[0] - render_out.attr[0, :, :, 0] > 0.3
+            )
+            occ_boundary = get_occulusion_boundary(
+                render_out.depth[0], dilation=0, thresh=0.1
+            )
+            # shrink to remove uv seam
+            # enlarge the mask gives better results
+            # remove the occlusion boundary to avoid bleeding
+            inpaint_mask = enlarge_mask(shrink_mask(inpaint_mask, 3), 5) & ~occ_boundary
+            if debug_dir is not None and debug_visualize_details:
+                tensor_to_image(occ_boundary).save(
+                    os.path.join(debug_dir, f"{mod_name}_occ_boundary_{i:02d}.jpg")
+                )
+
+            with mesh_use_texture(mesh, texture_update):
+                inpaint_image = render(
+                    self.ctx,
+                    mesh,
+                    best_cam,
+                    height=inpaint_render_size,
+                    width=inpaint_render_size,
+                    texture_filter_mode="linear",
+                ).attr[0]
+
+            # according to https://github.com/advimman/lama/issues/262
+            # blur the sharp edge mask gives better results
+            # but it gives worse results in our case
+            # inpaint_mask = blur_mask(inpaint_mask, 1)
+
+            inpaint_mask_input = inpaint_mask.float()[None, None]
+            inpaint_image_input = inpaint_image.permute(2, 0, 1)[None]
+            inpaint_result = inpaint_func(inpaint_image_input, inpaint_mask_input)[
+                0
+            ].permute(1, 2, 0)
+
+            if debug_dir is not None:
+                make_image_grid(
+                    [
+                        tensor_to_image(inpaint_image),
+                        tensor_to_image(inpaint_mask),
+                        tensor_to_image(inpaint_result),
+                    ],
+                    rows=1,
+                ).save(
+                    os.path.join(debug_dir, f"{mod_name}_inpaint_result_{i:02d}.jpg")
+                )
+            with mesh_use_texture(mesh, texture_update):
+                proj_out = self.cam_proj(
+                    inpaint_result[None],
+                    mesh,
+                    best_cam,
+                    masks=inpaint_mask[None].float(),
+                    from_scratch=False,
+                    poisson_blending=False,
+                    depth_grad_dilation=3,
+                    uv_exp_blend_alpha=3,
+                    aoi_cos_valid_threshold=0.1,
+                    uv_size=mesh.uv_size,
+                    uv_padding=True,
+                    iou_rejection_threshold=None,
+                    return_dict=True,
+                )
+            uv_proj_inpaint, uv_valid_mask_inpaint = (
+                proj_out.uv_proj,
+                proj_out.uv_proj_mask,
+            )
+            if debug_dir is not None and debug_visualize_details:
+                tensor_to_image(uv_valid_mask_inpaint).save(
+                    os.path.join(debug_dir, f"{mod_name}_uv_inpaint_mask_{i:02d}.jpg")
+                )
+            texture_update = uv_proj_inpaint
+            uv_valid_mask_update = uv_valid_mask_inpaint | uv_valid_mask_update
+            score_map_inpaint = torch.where(
+                uv_valid_mask_inpaint,
+                proj_out.uv_aoi_cos[0],
+                torch.zeros_like(proj_out.uv_aoi_cos[0]),
+            )
+            score_map = torch.max(score_map_update, score_map_inpaint)
+            if debug_dir is not None and debug_visualize_details:
+                make_image_grid(
+                    [
+                        tensor_to_image(score_map_update),
+                        tensor_to_image(score_map_inpaint),
+                        tensor_to_image(score_map),
+                    ]
+                ).save(os.path.join(debug_dir, f"{mod_name}_score_map_{i:02d}.jpg"))
+            score_map_update = score_map
+            i += 1
+
+        if uv_padding_end:
+            texture_update = uv_padding(texture_update, uv_valid_mask_update, 3)
+
+        return texture_update, uv_valid_mask_update
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/utils.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/utils.py
new file mode 100644
index 0000000..fcb965f
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/utils.py
@@ -0,0 +1,139 @@
+import io
+import math
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List, Optional, Union
+
+import numpy as np
+import nvdiffrast.torch as dr
+import torch
+import torch.nn.functional as F
+import trimesh
+from PIL import Image
+from torch import BoolTensor, FloatTensor
+
+LIST_TYPE = Union[list, np.ndarray, torch.Tensor]
+IMAGE_TYPE = Union[Image.Image, List[Image.Image], np.ndarray, torch.Tensor]
+SINGLE_IMAGE_TYPE = Union[Image.Image, np.ndarray, torch.Tensor]
+
+
+def tensor_to_image(
+    data: Union[Image.Image, torch.Tensor, np.ndarray],
+    batched: bool = False,
+    format: str = "HWC",
+) -> Union[Image.Image, List[Image.Image]]:
+    if isinstance(data, Image.Image):
+        return data
+    if isinstance(data, torch.Tensor):
+        data = data.detach().cpu().numpy()
+    if data.dtype == np.float32 or data.dtype == np.float16:
+        data = (data * 255).astype(np.uint8)
+    elif data.dtype == np.bool_:
+        data = data.astype(np.uint8) * 255
+    assert data.dtype == np.uint8
+    if format == "CHW":
+        if batched and data.ndim == 4:
+            data = data.transpose((0, 2, 3, 1))
+        elif not batched and data.ndim == 3:
+            data = data.transpose((1, 2, 0))
+
+    if batched:
+        return [Image.fromarray(d) for d in data]
+    return Image.fromarray(data)
+
+
+def image_to_tensor(image: IMAGE_TYPE, return_type="pt", device: Optional[str] = None):
+    assert return_type in ["np", "pt"]
+    batched = True
+    if isinstance(image, Image.Image):
+        batched = False
+        image = [image]
+    if isinstance(image, list):
+        image = np.stack([np.array(img) for img in image], axis=0)
+        image = image.astype(np.float32) / 255.0
+    if isinstance(image, np.ndarray) and return_type == "pt":
+        image = torch.tensor(image, device=device)
+    if isinstance(image, torch.Tensor):
+        image = image.to(dtype=torch.float32, device=device)
+
+    if not batched:
+        image = image[0]
+    return image
+
+
+def largest_factor_near_sqrt(n: int) -> int:
+    """
+    Finds the largest factor of n that is closest to the square root of n.
+
+    Args:
+        n (int): The integer for which to find the largest factor near its square root.
+
+    Returns:
+        int: The largest factor of n that is closest to the square root of n.
+    """
+    sqrt_n = int(math.sqrt(n))  # Get the integer part of the square root
+
+    # First, check if the square root itself is a factor
+    if sqrt_n * sqrt_n == n:
+        return sqrt_n
+
+    # Otherwise, find the largest factor by iterating from sqrt_n downwards
+    for i in range(sqrt_n, 0, -1):
+        if n % i == 0:
+            return i
+
+    # If n is 1, return 1
+    return 1
+
+
+def make_image_grid(
+    images: List[Image.Image],
+    rows: Optional[int] = None,
+    cols: Optional[int] = None,
+    resize: Optional[int] = None,
+) -> Image.Image:
+    """
+    Prepares a single grid of images. Useful for visualization purposes.
+    """
+    if rows is None and cols is not None:
+        assert len(images) % cols == 0
+        rows = len(images) // cols
+    elif cols is None and rows is not None:
+        assert len(images) % rows == 0
+        cols = len(images) // rows
+    elif rows is None and cols is None:
+        rows = largest_factor_near_sqrt(len(images))
+        cols = len(images) // rows
+
+    assert len(images) == rows * cols
+
+    if resize is not None:
+        images = [img.resize((resize, resize)) for img in images]
+
+    w, h = images[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+
+    for i, img in enumerate(images):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+def get_current_timestamp(fmt: str = "%Y%m%d%H%M%S") -> str:
+    return datetime.now().strftime(fmt)
+
+
+def get_clip_space_position(pos: torch.FloatTensor, mvp_mtx: torch.FloatTensor):
+    pos_homo = torch.cat([pos, torch.ones([pos.shape[0], 1]).to(pos)], dim=-1)
+    return torch.matmul(pos_homo, mvp_mtx.permute(0, 2, 1))
+
+
+def transform_points_homo(pos: torch.FloatTensor, mtx: torch.FloatTensor):
+    batch_size = pos.shape[0]
+    pos_shape = pos.shape[1:-1]
+    pos = pos.reshape(batch_size, -1, 3)
+    pos_homo = torch.cat([pos, torch.ones_like(pos[..., 0:1])], dim=-1)
+    pos = (pos_homo.unsqueeze(2) * mtx.unsqueeze(1)).sum(-1)[..., :3]
+    pos = pos.reshape(batch_size, *pos_shape, 3)
+    return pos
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/uv.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/uv.py
new file mode 100644
index 0000000..040ceee
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/uv.py
@@ -0,0 +1,468 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+from .blend import PoissonBlendingSolver
+from .camera import Camera
+from .mesh import TexturedMesh
+from .render import NVDiffRastContextWrapper, SimpleNormalization, render
+from .utils import IMAGE_TYPE, get_clip_space_position, image_to_tensor
+
+
+@dataclass
+class UVPrecomputeOutput:
+    height: int
+    width: int
+    uv_attr: torch.Tensor
+    uv_mask: torch.Tensor
+    uv_pos: torch.Tensor
+
+
+def uv_precompute(
+    ctx: NVDiffRastContextWrapper,
+    mesh: TexturedMesh,
+    height: int,
+    width: int,
+    clone_attr: bool = False,
+) -> UVPrecomputeOutput:
+    uv_clip = mesh.v_tex * 2.0 - 1.0
+    uv_clip4 = torch.cat(
+        (
+            uv_clip,
+            torch.zeros_like(uv_clip[..., 0:1]),
+            torch.ones_like(uv_clip[..., 0:1]),
+        ),
+        dim=-1,
+    )
+    rast, _ = ctx.rasterize(uv_clip4[None], mesh.t_tex_idx, (height, width))
+    uv_mask = rast[0, :, :, 3] > 0
+
+    uv_pos, _ = ctx.interpolate(mesh.v_pos[None], rast, mesh.t_pos_idx)
+    uv_pos = uv_pos[0]
+
+    uv_precompute_output = UVPrecomputeOutput(
+        height=height,
+        width=width,
+        uv_attr=mesh.texture.clone() if clone_attr else mesh.texture,
+        uv_mask=uv_mask,
+        uv_pos=uv_pos,
+    )
+    return uv_precompute_output
+
+
+@dataclass
+class UVRenderGeometryOutput:
+    uv_pos_proj: torch.Tensor
+    uv_pos_error: torch.Tensor
+    uv_aoi_cos: torch.Tensor
+    uv_pos_ndc: torch.Tensor
+    view_mask: torch.Tensor
+    view_normal: torch.Tensor
+    view_aoi_cos: torch.Tensor
+    view_position: torch.Tensor
+    view_depth: torch.Tensor
+    view_depth_grad: Optional[torch.Tensor] = None
+    uv_depth_grad: Optional[torch.Tensor] = None
+    view_attr: Optional[torch.Tensor] = None
+
+
+def uv_render_geometry(
+    ctx: NVDiffRastContextWrapper,
+    mesh: TexturedMesh,
+    cam: Camera,
+    view_height: int,
+    view_width: int,
+    uv_precompute_output: UVPrecomputeOutput,
+    grid_sample_mode="bilinear",
+    compute_depth_grad: bool = False,
+    depth_grad_dilation: int = 1,
+    render_attr: bool = False,
+) -> UVRenderGeometryOutput:
+    batch_size = cam.c2w.shape[0]
+
+    height, width, _ = uv_precompute_output.uv_pos.shape
+    uv_pos_clip = get_clip_space_position(
+        uv_precompute_output.uv_pos.view(-1, 3), cam.mvp_mtx
+    ).view(batch_size, height, width, 4)
+    uv_pos_ndc = uv_pos_clip[..., :2] / uv_pos_clip[..., 3:4]
+
+    render_output = render(
+        ctx,
+        mesh,
+        cam,
+        view_height,
+        view_width,
+        render_attr=render_attr,
+        render_depth=True,
+        render_normal=True,
+        depth_normalization_strategy=SimpleNormalization(
+            scale=1.0, offset=0.0, clamp=False, bg_value=1e2
+        ),
+    )
+    view_position = render_output.pos
+    view_mask = render_output.mask
+    view_normal = render_output.normal
+    view_normal_cs = (
+        view_normal[:, :, :, None, :] * cam.w2c[:, None, None, :3, :3]
+    ).sum(-1)
+    view_normal_cs = F.normalize(view_normal_cs, dim=-1, p=2)
+    view_normal_cs[~render_output.mask] = render_output.normal[~render_output.mask]
+    view_aoi_cos = (
+        view_normal_cs
+        * torch.as_tensor(
+            [0.0, 0.0, 1.0], dtype=view_normal_cs.dtype, device=view_normal_cs.device
+        )[None, None, None]
+    ).sum(-1)
+    view_aoi_cos = view_aoi_cos.clamp(0.0, 1.0)
+
+    view_depth = render_output.depth
+    if compute_depth_grad:
+        grad_x_kernel = torch.tensor(
+            [[1, 0, -1], [2, 0, -2], [1, 0, -1]],
+            device=view_depth.device,
+            dtype=torch.float32,
+        ).view(1, 1, 3, 3)
+        grad_y_kernel = torch.tensor(
+            [[1, 2, 1], [0, 0, 0], [-1, -2, -1]],
+            device=view_depth.device,
+            dtype=torch.float32,
+        ).view(1, 1, 3, 3)
+        view_depth_grad_x = F.conv2d(view_depth[:, None], grad_x_kernel, padding=1)
+        view_depth_grad_y = F.conv2d(view_depth[:, None], grad_y_kernel, padding=1)
+        view_depth_grad = (view_depth_grad_x**2 + view_depth_grad_y**2).sqrt()
+        view_depth_grad = F.max_pool2d(
+            view_depth_grad,
+            depth_grad_dilation,
+            stride=1,
+            padding=depth_grad_dilation // 2,
+        )
+
+        uv_depth_grad = F.grid_sample(
+            view_depth_grad, uv_pos_ndc, align_corners=False, mode=grid_sample_mode
+        ).permute(0, 2, 3, 1)[..., 0]
+    else:
+        view_depth_grad = None
+        uv_depth_grad = None
+
+    if render_attr:
+        view_attr = render_output.attr
+    else:
+        view_attr = None
+
+    uv_pos_proj = F.grid_sample(
+        view_position.permute(0, 3, 1, 2),
+        uv_pos_ndc,
+        align_corners=False,
+        mode=grid_sample_mode,
+    ).permute(0, 2, 3, 1)
+
+    uv_pos_error = ((uv_pos_proj - uv_precompute_output.uv_pos) ** 2).sum(-1).sqrt()
+
+    uv_aoi_cos = F.grid_sample(
+        view_aoi_cos[..., None].permute(0, 3, 1, 2),
+        uv_pos_ndc,
+        align_corners=False,
+        mode=grid_sample_mode,
+    ).permute(0, 2, 3, 1)[..., 0]
+
+    return UVRenderGeometryOutput(
+        uv_pos_proj=uv_pos_proj,
+        uv_pos_error=uv_pos_error,
+        uv_aoi_cos=uv_aoi_cos,
+        uv_pos_ndc=uv_pos_ndc,
+        view_mask=view_mask,
+        view_position=view_position,
+        view_normal=view_normal,
+        view_aoi_cos=view_aoi_cos,
+        view_depth=view_depth,
+        view_depth_grad=view_depth_grad,
+        uv_depth_grad=uv_depth_grad,
+        view_attr=view_attr,
+    )
+
+
+@dataclass
+class UVRenderAttrOutput:
+    uv_attr_proj: torch.Tensor
+    uv_mask_proj: Optional[torch.Tensor]
+
+
+def uv_render_attr(
+    images: IMAGE_TYPE,
+    uv_render_geometry_output: UVRenderGeometryOutput,
+    masks: Optional[IMAGE_TYPE] = None,
+    grid_sample_mode: str = "bilinear",
+) -> UVRenderAttrOutput:
+    images = image_to_tensor(images, device=uv_render_geometry_output.uv_pos_ndc.device)
+    uv_attr_proj = F.grid_sample(
+        images.permute(0, 3, 1, 2),
+        uv_render_geometry_output.uv_pos_ndc,
+        align_corners=False,
+        mode=grid_sample_mode,
+    ).permute(0, 2, 3, 1)
+
+    if masks is not None:
+        masks = image_to_tensor(
+            masks, device=uv_render_geometry_output.uv_pos_ndc.device
+        )
+        if masks.ndim == 4:
+            masks = masks.mean(-1)
+        uv_mask_proj = F.grid_sample(
+            masks[..., None].permute(0, 3, 1, 2),
+            uv_render_geometry_output.uv_pos_ndc,
+            align_corners=False,
+            mode=grid_sample_mode,
+        ).permute(0, 2, 3, 1)[..., 0]
+    else:
+        uv_mask_proj = None
+
+    return UVRenderAttrOutput(uv_attr_proj=uv_attr_proj, uv_mask_proj=uv_mask_proj)
+
+
+@dataclass
+class UVBlendOutput:
+    uv_attr_blend: Optional[torch.Tensor]
+    uv_valid_mask: torch.Tensor
+    uv_valid_mask_blend: torch.Tensor
+    uv_blend_weight: torch.Tensor
+
+
+class UVValidityStrategy(ABC):
+    @abstractmethod
+    def __init__(self, *args, **kwargs):
+        pass
+
+    @abstractmethod
+    def __call__(
+        self,
+        uv_precompute_output: UVPrecomputeOutput,
+        uv_render_geometry_output: UVRenderGeometryOutput,
+        uv_render_attr_output: Optional[UVRenderAttrOutput],
+    ) -> torch.BoolTensor:
+        pass
+
+
+class SimpleUVValidityStrategy(UVValidityStrategy):
+    def __init__(
+        self,
+        pos_error_eps: float = 1e-3,
+        aoi_cos_thresh: float = 0.1,
+        mask_thresh: float = 0.9,
+        depth_grad_thresh: Optional[float] = None,
+        first_view_dominate: bool = False,
+    ):
+        self.pos_error_eps = pos_error_eps
+        self.aoi_cos_thresh = aoi_cos_thresh
+        self.mask_thresh = mask_thresh
+        self.depth_grad_thresh = depth_grad_thresh
+        self.first_view_dominate = first_view_dominate
+
+    def __call__(
+        self,
+        uv_precompute_output: UVPrecomputeOutput,
+        uv_render_geometry_output: UVRenderGeometryOutput,
+        uv_render_attr_output: Optional[UVRenderAttrOutput],
+    ) -> torch.BoolTensor:
+        uv_pos_valid = uv_render_geometry_output.uv_pos_error < self.pos_error_eps
+        uv_aoi_cos_valid = uv_render_geometry_output.uv_aoi_cos > self.aoi_cos_thresh
+        uv_valid_mask = uv_pos_valid & uv_aoi_cos_valid
+
+        if self.depth_grad_thresh is not None:
+            if uv_render_geometry_output.uv_depth_grad is None:
+                print(
+                    "Warning: Depth gradient is not computed, depth gradient threshold is ignored."
+                )
+            else:
+                uv_valid_mask &= (
+                    uv_render_geometry_output.uv_depth_grad < self.depth_grad_thresh
+                )
+
+        uv_valid_mask &= uv_precompute_output.uv_mask
+
+        if (
+            uv_render_attr_output is not None
+            and uv_render_attr_output.uv_mask_proj is not None
+        ):
+            uv_valid_mask &= uv_render_attr_output.uv_mask_proj > self.mask_thresh
+        else:
+            print("No view mask provided for UV blending, using all valid pixels")
+
+        if self.first_view_dominate:
+            uv_valid_mask[1:][
+                uv_valid_mask[0:1].expand(uv_valid_mask.shape[0] - 1, -1, -1)
+            ] = False
+
+        return uv_valid_mask
+
+
+class UVBlendWeightStrategy(ABC):
+    @abstractmethod
+    def __init__(self, *args, **kwargs):
+        pass
+
+    @abstractmethod
+    def __call__(
+        self,
+        uv_precompute_output: UVPrecomputeOutput,
+        uv_render_geometry_output: UVRenderGeometryOutput,
+        uv_render_attr_output: Optional[UVRenderAttrOutput],
+        uv_valid_mask: torch.BoolTensor,
+    ) -> torch.FloatTensor:
+        pass
+
+
+class ExponentialBlend(UVBlendWeightStrategy):
+    def __init__(
+        self,
+        alpha: float = 1.0,
+        normalization: str = "linear",
+        view_weight: Optional[torch.FloatTensor] = None,
+    ):
+        self.alpha = alpha
+        self.normalization = normalization
+        self.view_weight = view_weight
+
+    def __call__(
+        self,
+        uv_precompute_output: UVPrecomputeOutput,
+        uv_render_geometry_output: UVRenderGeometryOutput,
+        uv_render_attr_output: Optional[UVRenderAttrOutput],
+        uv_valid_mask: torch.BoolTensor,
+    ) -> torch.FloatTensor:
+        weight = uv_render_geometry_output.uv_aoi_cos
+        weight = weight * uv_valid_mask.float()
+        if self.view_weight is not None:
+            weight = weight ** (self.alpha / self.view_weight[:, None, None].to(weight))
+        else:
+            weight = weight**self.alpha
+        if self.normalization == "linear":
+            weight_norm = (weight / weight.sum(axis=0, keepdim=True).clamp(1e-5)).clamp(
+                0.0, 1.0
+            )
+        elif self.normalization == "softmax":
+            weight[~uv_valid_mask] = -1e5
+            weight_norm = F.softmax(weight, dim=0)
+        return weight_norm
+
+
+class RandomChoiceBlend(UVBlendWeightStrategy):
+    def __init__(self, alpha):
+        self.alpha = alpha
+
+    def __call__(
+        self,
+        uv_precompute_output: UVPrecomputeOutput,
+        uv_render_geometry_output: UVRenderGeometryOutput,
+        uv_render_attr_output: Optional[UVRenderAttrOutput],
+        uv_valid_mask: torch.BoolTensor,
+    ) -> torch.FloatTensor:
+        weight = uv_render_geometry_output.uv_aoi_cos
+        weight = weight * uv_valid_mask.float()
+        weight[weight > 0] = torch.rand_like(weight[weight > 0])
+        weight_norm = (
+            F.one_hot(weight.max(dim=0).indices, num_classes=weight.shape[0])
+            .float()
+            .permute(2, 0, 1)
+        )
+        return weight_norm
+
+
+def uv_padding(attr: torch.FloatTensor, inside_mask: torch.BoolTensor, radius: int):
+    if attr.min() < -1e-5 or attr.max() > 1 + 1e-5:
+        print(
+            "Warning: UV blending result is out of range [0, 1], force clamped before UV padding."
+        )
+
+    from .cv_ops import inpaint_torch
+
+    attr_padded = inpaint_torch(attr.clamp(0.0, 1.0), ~inside_mask, radius)
+    return attr_padded
+
+
+def uv_blend(
+    uv_precompute_output: UVPrecomputeOutput,
+    uv_render_geometry_output: UVRenderGeometryOutput,
+    uv_render_attr_output: Optional[UVRenderAttrOutput],
+    uv_validity_strategy: UVValidityStrategy = SimpleUVValidityStrategy(),
+    uv_blend_weight_strategy: UVBlendWeightStrategy = ExponentialBlend(),
+    empty_value: float = 0.0,
+    do_uv_padding: bool = True,
+    uv_padding_radius: int = 3,
+    pad_unseen_area: bool = False,
+    poisson_blending: bool = False,
+    pb_solver: Optional[PoissonBlendingSolver] = None,
+    pb_num_iters: int = 1000,
+    pb_keep_original_border: bool = True,
+    pb_inplace: bool = False,
+    pb_grad_mode: str = "src",
+) -> UVBlendOutput:
+    uv_valid_mask = uv_validity_strategy(
+        uv_precompute_output, uv_render_geometry_output, uv_render_attr_output
+    )
+    uv_blend_weight = uv_blend_weight_strategy(
+        uv_precompute_output,
+        uv_render_geometry_output,
+        uv_render_attr_output,
+        uv_valid_mask,
+    )
+    uv_valid_mask_blend = uv_valid_mask.any(dim=0)
+
+    if uv_render_attr_output is None:
+        return UVBlendOutput(
+            uv_attr_blend=None,
+            uv_valid_mask=uv_valid_mask,
+            uv_valid_mask_blend=uv_valid_mask_blend,
+            uv_blend_weight=uv_blend_weight,
+        )
+
+    uv_attr_blend = (
+        uv_render_attr_output.uv_attr_proj * uv_blend_weight[..., None]
+    ).sum(axis=0)
+    if poisson_blending:
+        assert do_uv_padding
+        assert pb_solver is not None
+        uv_attr_blend_padded = uv_padding(
+            uv_attr_blend, uv_valid_mask_blend, uv_padding_radius
+        )
+        if pb_keep_original_border:
+            pb_tgt = uv_precompute_output.uv_attr
+        else:
+            uv_attr_hard_stitch = (
+                uv_attr_blend * uv_valid_mask_blend[..., None].float()
+                + uv_precompute_output.uv_attr
+                * (~uv_valid_mask_blend)[..., None].float()
+            )
+            uv_attr_hard_stitch_padded = uv_padding(
+                uv_attr_hard_stitch, uv_precompute_output.uv_mask, uv_padding_radius
+            )
+            pb_tgt = uv_attr_hard_stitch_padded
+
+        uv_attr_blend = pb_solver(
+            uv_attr_blend_padded,
+            uv_valid_mask_blend,
+            pb_tgt,
+            pb_num_iters,
+            inplace=pb_inplace,
+            grad_mode=pb_grad_mode,
+        )
+    else:
+        uv_attr_blend = (
+            uv_attr_blend * uv_valid_mask_blend[..., None].float()
+            + uv_precompute_output.uv_attr * (~uv_valid_mask_blend)[..., None].float()
+        )
+
+    if do_uv_padding:
+        content_mask = (
+            uv_valid_mask_blend if pad_unseen_area else uv_precompute_output.uv_mask
+        )
+        uv_attr_blend = uv_padding(uv_attr_blend, content_mask, uv_padding_radius)
+
+    return UVBlendOutput(
+        uv_attr_blend=uv_attr_blend,
+        uv_valid_mask=uv_valid_mask,
+        uv_valid_mask_blend=uv_valid_mask_blend,
+        uv_blend_weight=uv_blend_weight,
+    )
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/warp.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/warp.py
new file mode 100755
index 0000000..021e158
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/mesh_utils/warp.py
@@ -0,0 +1,285 @@
+import os
+
+import numpy as np
+import nvdiffrast.torch as dr
+import torch
+import torch.nn.functional as F
+import trimesh
+from einops import rearrange
+from PIL import Image, ImageDraw
+from tqdm import tqdm
+
+
+def draw(img, lines):
+    """
+    lines: n x 4, x1,y2,x2,y2
+    """
+    if isinstance(img, torch.Tensor):
+        img = img.detach().cpu().numpy()
+
+    if isinstance(img, np.ndarray):
+        if img.dtype == np.float32 or img.dtype == np.float64:
+            img = (img * 255.0).astype(np.uint8)
+        img = Image.fromarray(img)
+
+    img = img.resize((512, 512), Image.Resampling.BICUBIC)
+    w, h = img.size
+    assert w == h
+    if isinstance(lines, torch.Tensor):
+        lines = lines.detach().cpu().numpy()
+    lines = (lines + 1) / 2 * w
+
+    img1 = ImageDraw.Draw(img)
+    for line in lines:
+        img1.line(line.tolist(), fill="red", width=1)
+    return img
+
+
+def construct_grid_mesh(n_grid):
+    vertices = []
+    vertices_movable_ids = []
+    idx = 0
+    for j in range(n_grid + 1):
+        for i in range(n_grid + 1):
+            # 3D fit in trimesh format
+            if 0 < i < n_grid and 0 < j < n_grid:
+                vertices_movable_ids.append(idx)
+            vertices.append([i / n_grid, j / n_grid, 1.0 / 2])
+            idx += 1
+
+    vertices = np.array(vertices)
+    vertices = 2 * vertices - 1
+
+    faces = []
+    for j in range(n_grid):
+        for i in range(n_grid):
+            # clockwise
+            faces.append(
+                [
+                    i + j * (n_grid + 1),
+                    i + 1 + j * (n_grid + 1),
+                    i + (j + 1) * (n_grid + 1),
+                ]
+            )
+            faces.append(
+                [
+                    i + 1 + j * (n_grid + 1),
+                    i + 1 + (j + 1) * (n_grid + 1),
+                    i + (j + 1) * (n_grid + 1),
+                ]
+            )
+    faces = np.array(faces)
+    vertices_movable_ids = np.array(vertices_movable_ids)
+
+    mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)
+    return mesh, vertices_movable_ids, vertices
+
+
+def compute_warp_field(
+    ctx,
+    src_images_tensor,
+    tgt_images_tensor,
+    n_grid,
+    optim_res,
+    optim_step_per_res,
+    lambda_reg,
+    temp_dir,
+    verbose,
+    device,
+):
+    """
+    src_images_tensor: 4 H W 4
+    tgt_images_tensor: 4 H W 4
+    return: 4 H W 4
+    """
+    lam_reg = lambda_reg
+    lam_mask = 2
+    mesh, _, vertices_np = construct_grid_mesh(n_grid)
+
+    # vertices = mesh.vertices
+    faces = mesh.faces
+    edges = mesh.edges_unique
+
+    n_degree = torch.tensor(mesh.vertex_degree)
+    vertices = torch.tensor(vertices_np, device=device, dtype=torch.float32)
+    vertices_unopt = vertices.clone()
+    faces = torch.tensor(faces, device=device)
+    edges = torch.tensor(edges, device=device)
+
+    warped_images_tensor = []
+    bs = src_images_tensor.shape[0]
+
+    for img_idx in range(bs):
+        src_image_tensor = src_images_tensor[img_idx]
+        tgt_image_tensor = tgt_images_tensor[img_idx]
+
+        if verbose and temp_dir is not None:
+            vis_dir = os.path.join(temp_dir, f"{img_idx}")
+            os.makedirs(vis_dir, exist_ok=True)
+        # prepare for nvdiff rendering
+        v_origin_homo = torch.cat(
+            [vertices_unopt, torch.ones_like(vertices_unopt[..., :1])], dim=-1
+        )
+
+        # prepare parameters
+        v_move_indices = torch.where(n_degree == 6)[0]
+        vertices_movable = vertices[v_move_indices].detach().clone()
+        vertices_movable.requires_grad = True
+        opt = torch.optim.Adam([vertices_movable], lr=0.02)
+
+        for resl in optim_res:
+            rast, _ = dr.rasterize(
+                ctx,
+                v_origin_homo[None, ...].float(),
+                faces.int(),
+                (resl, resl),
+                grad_db=True,
+            )
+            face_ids = rast[..., 3].long() - 1
+            assert torch.all(face_ids >= 0)
+            u = rast[..., 0]
+            v = rast[..., 1]
+            pixel_vertice_ids = faces[face_ids]
+            pixel_bary_coords = torch.stack([u, v, 1 - u - v], dim=-1)
+
+            src_img = F.interpolate(
+                rearrange(src_image_tensor[..., :3], "H W C -> () C H W"),
+                size=(resl, resl),
+                mode="bilinear",
+                align_corners=False,
+                antialias=True,
+            )
+
+            tgt_img = F.interpolate(
+                rearrange(tgt_image_tensor[..., :3], "H W C -> () C H W"),
+                size=(resl, resl),
+                mode="bilinear",
+                align_corners=False,
+                antialias=True,
+            )
+
+            # src_img, tgt_img: 1 C H W
+            if verbose:
+                Image.fromarray(
+                    (tgt_img[0].permute(1, 2, 0).detach().cpu().numpy() * 255.0).astype(
+                        np.uint8
+                    )
+                ).save(os.path.join(vis_dir, f"target_{resl:04d}.png"))
+            with tqdm(range(optim_step_per_res), disable=not verbose) as pbar:
+                for i in pbar:
+                    opt.zero_grad()
+                    vertices_all = vertices_unopt.detach().clone()
+                    vertices_all[v_move_indices] = vertices_movable
+
+                    pixel_vertices = vertices_all[
+                        pixel_vertice_ids
+                    ]  # 1 x 512 x 512 x 3 x 3
+                    pixel_coords_inter = torch.sum(
+                        pixel_vertices * pixel_bary_coords[..., None], dim=-2
+                    )
+                    src_img_warped = F.grid_sample(
+                        src_img,
+                        pixel_coords_inter[..., :2],
+                        mode="bilinear",
+                        align_corners=False,
+                    )
+
+                    edge_vertices_all = vertices_all[edges]
+                    edge_vertices_unopt = vertices_unopt[edges]
+
+                    edge_len_all = torch.linalg.norm(
+                        edge_vertices_all[:, 0, :2] - edge_vertices_all[:, 1, :2],
+                        dim=-1,
+                    )
+                    edge_len_unopt = torch.linalg.norm(
+                        edge_vertices_unopt[:, 0, :2] - edge_vertices_all[:, 1, :2],
+                        dim=-1,
+                    )
+
+                    reg_loss = ((edge_len_all - edge_len_unopt) ** 2).mean()
+
+                    # mask_loss = ((src_img_warped[:, 3:4, :, :] - tgt_img[:, 3:4, :, :])**2).mean()
+                    # rgb_loss = (((src_img_warped[:, :3, :, :] - tgt_img[:, :3, :, :]) * tgt_img[:, 3:4, :, :])**2).mean()
+                    # loss = rgb_loss + lam_reg * reg_loss + lam_mask * mask_loss
+                    img_loss = ((src_img_warped - tgt_img) ** 2).mean()
+                    loss = img_loss + lam_reg * reg_loss
+
+                    loss.backward()
+                    opt.step()
+                    if verbose:
+                        print(img_loss.item(), reg_loss.item())
+
+                    if verbose:
+                        draw(
+                            src_img[0].permute(1, 2, 0),
+                            edge_vertices_all[:, :, :2].reshape(-1, 4),
+                        ).save(os.path.join(vis_dir, f"src_{resl:04d}_{i:03d}.png"))
+                        Image.fromarray(
+                            (
+                                torch.cat(
+                                    [
+                                        tgt_img,
+                                        src_img_warped,
+                                        (tgt_img - src_img_warped).abs(),
+                                    ],
+                                    dim=-1,
+                                )[0]
+                                .permute(1, 2, 0)
+                                .detach()
+                                .cpu()
+                                .numpy()
+                                * 255.0
+                            ).astype(np.uint8)
+                        ).save(os.path.join(vis_dir, f"opt_{resl:04d}_{i:03d}.png"))
+                        warped_this_resl = Image.fromarray(
+                            (
+                                src_img_warped[0]
+                                .permute(1, 2, 0)
+                                .detach()
+                                .cpu()
+                                .numpy()
+                                * 255.0
+                            ).astype(np.uint8)
+                        )
+                        warped_this_resl.save(
+                            os.path.join(vis_dir, f"warped_{resl:04d}_{i:03d}.png")
+                        )
+
+        # warp on 512 resolution
+        resl = src_image_tensor.shape[1]
+        with torch.no_grad():
+            rast, _ = dr.rasterize(
+                ctx,
+                v_origin_homo[None, ...].float(),
+                faces.int(),
+                (resl, resl),
+                grad_db=True,
+            )
+            face_ids = rast[..., 3].long() - 1
+            assert torch.all(face_ids >= 0)
+            u = rast[..., 0]
+            v = rast[..., 1]
+            pixel_vertice_ids = faces[face_ids]
+            pixel_bary_coords = torch.stack([u, v, 1 - u - v], dim=-1)
+
+            vertices_all = vertices_unopt.detach().clone()
+            vertices_all[v_move_indices] = vertices_movable
+
+            pixel_vertices = vertices_all[pixel_vertice_ids]  # 1 x 512 x 512 x 3 x 3
+            pixel_coords_inter = torch.sum(
+                pixel_vertices * pixel_bary_coords[..., None], dim=-2
+            )
+            src_img_warped = rearrange(
+                F.grid_sample(
+                    rearrange(src_image_tensor, "H W C -> () C H W"),
+                    pixel_coords_inter[..., :2],
+                    mode="bicubic",
+                    align_corners=False,
+                ),
+                "() C H W -> H W C",
+            ).clamp(0, 1)
+        warped_images_tensor.append(src_img_warped)
+
+    warped_images_tensor = torch.stack(warped_images_tensor, dim=0)  # 4 H W 3
+
+    return warped_images_tensor
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/misc.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/misc.py
new file mode 100644
index 0000000..e29a9b7
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/misc.py
@@ -0,0 +1,220 @@
+import gc
+import os
+import re
+import time
+from collections import defaultdict
+from contextlib import contextmanager
+
+import psutil
+import torch
+from packaging import version
+
+from .config import config_to_primitive
+from .core import debug, find, info, warn
+from .typing import *
+
+
+def parse_version(ver: str):
+    return version.parse(ver)
+
+
+def get_rank():
+    # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing,
+    # therefore LOCAL_RANK needs to be checked first
+    rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK")
+    for key in rank_keys:
+        rank = os.environ.get(key)
+        if rank is not None:
+            return int(rank)
+    return 0
+
+
+def get_device():
+    return torch.device(f"cuda:{get_rank()}")
+
+
+def load_module_weights(
+    path, module_name=None, ignore_modules=None, mapping=None, map_location=None
+) -> Tuple[dict, int, int]:
+    if module_name is not None and ignore_modules is not None:
+        raise ValueError("module_name and ignore_modules cannot be both set")
+    if map_location is None:
+        map_location = get_device()
+
+    ckpt = torch.load(path, map_location=map_location)
+    state_dict = ckpt["state_dict"]
+
+    if mapping is not None:
+        state_dict_to_load = {}
+        for k, v in state_dict.items():
+            if any([k.startswith(m["to"]) for m in mapping]):
+                pass
+            else:
+                state_dict_to_load[k] = v
+        for k, v in state_dict.items():
+            for m in mapping:
+                if k.startswith(m["from"]):
+                    k_dest = k.replace(m["from"], m["to"])
+                    info(f"Mapping {k} => {k_dest}")
+                    state_dict_to_load[k_dest] = v.clone()
+        state_dict = state_dict_to_load
+
+    state_dict_to_load = state_dict
+
+    if ignore_modules is not None:
+        state_dict_to_load = {}
+        for k, v in state_dict.items():
+            ignore = any(
+                [k.startswith(ignore_module + ".") for ignore_module in ignore_modules]
+            )
+            if ignore:
+                continue
+            state_dict_to_load[k] = v
+
+    if module_name is not None:
+        state_dict_to_load = {}
+        for k, v in state_dict.items():
+            m = re.match(rf"^{module_name}\.(.*)$", k)
+            if m is None:
+                continue
+            state_dict_to_load[m.group(1)] = v
+
+    return state_dict_to_load, ckpt["epoch"], ckpt["global_step"]
+
+
+def C(value: Any, epoch: int, global_step: int) -> float:
+    if isinstance(value, int) or isinstance(value, float):
+        pass
+    else:
+        value = config_to_primitive(value)
+        if not isinstance(value, list):
+            raise TypeError("Scalar specification only supports list, got", type(value))
+        if len(value) == 3:
+            value = [0] + value
+        assert len(value) == 4
+        start_step, start_value, end_value, end_step = value
+        if isinstance(end_step, int):
+            current_step = global_step
+            value = start_value + (end_value - start_value) * max(
+                min(1.0, (current_step - start_step) / (end_step - start_step)), 0.0
+            )
+        elif isinstance(end_step, float):
+            current_step = epoch
+            value = start_value + (end_value - start_value) * max(
+                min(1.0, (current_step - start_step) / (end_step - start_step)), 0.0
+            )
+    return value
+
+
+def cleanup():
+    gc.collect()
+    torch.cuda.empty_cache()
+    try:
+        import tinycudann as tcnn
+
+        tcnn.free_temporary_memory()
+    except:
+        pass
+
+
+def finish_with_cleanup(func: Callable):
+    def wrapper(*args, **kwargs):
+        out = func(*args, **kwargs)
+        cleanup()
+        return out
+
+    return wrapper
+
+
+def _distributed_available():
+    return torch.distributed.is_available() and torch.distributed.is_initialized()
+
+
+def barrier():
+    if not _distributed_available():
+        return
+    else:
+        torch.distributed.barrier()
+
+
+def broadcast(tensor, src=0):
+    if not _distributed_available():
+        return tensor
+    else:
+        torch.distributed.broadcast(tensor, src=src)
+        return tensor
+
+
+def enable_gradient(model, enabled: bool = True) -> None:
+    for param in model.parameters():
+        param.requires_grad_(enabled)
+
+
+class TimeRecorder:
+    _instance = None
+
+    def __init__(self):
+        self.items = {}
+        self.accumulations = defaultdict(list)
+        self.time_scale = 1000.0  # ms
+        self.time_unit = "ms"
+        self.enabled = False
+
+    def __new__(cls):
+        # singleton
+        if cls._instance is None:
+            cls._instance = super(TimeRecorder, cls).__new__(cls)
+        return cls._instance
+
+    def enable(self, enabled: bool) -> None:
+        self.enabled = enabled
+
+    def start(self, name: str) -> None:
+        if not self.enabled:
+            return
+        torch.cuda.synchronize()
+        self.items[name] = time.time()
+
+    def end(self, name: str, accumulate: bool = False) -> float:
+        if not self.enabled or name not in self.items:
+            return
+        torch.cuda.synchronize()
+        start_time = self.items.pop(name)
+        delta = time.time() - start_time
+        if accumulate:
+            self.accumulations[name].append(delta)
+        t = delta * self.time_scale
+        info(f"{name}: {t:.2f}{self.time_unit}")
+
+    def get_accumulation(self, name: str, average: bool = False) -> float:
+        if not self.enabled or name not in self.accumulations:
+            return
+        acc = self.accumulations.pop(name)
+        total = sum(acc)
+        if average:
+            t = total / len(acc) * self.time_scale
+        else:
+            t = total * self.time_scale
+        info(f"{name} for {len(acc)} times: {t:.2f}{self.time_unit}")
+
+
+### global time recorder
+time_recorder = TimeRecorder()
+
+
+@contextmanager
+def time_recorder_enabled():
+    enabled = time_recorder.enabled
+    time_recorder.enable(enabled=True)
+    try:
+        yield
+    finally:
+        time_recorder.enable(enabled=enabled)
+
+
+def show_vram_usage(name):
+    available, total = torch.cuda.mem_get_info()
+    used = total - available
+    print(
+        f"{name}: {used / 1024**2:.1f}MB, {psutil.Process(os.getpid()).memory_info().rss / 1024**2:.1f}MB"
+    )
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/ops.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/ops.py
new file mode 100644
index 0000000..8272799
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/ops.py
@@ -0,0 +1,462 @@
+import math
+from collections import defaultdict
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.cuda.amp import custom_bwd, custom_fwd
+
+from .core import debug, find, info, warn
+from .typing import *
+
+
+def dot(x, y):
+    return torch.sum(x * y, -1, keepdim=True)
+
+
+def reflect(x, n):
+    return 2 * dot(x, n) * n - x
+
+
+ValidScale = Union[Tuple[float, float], Num[Tensor, "2 D"]]
+
+
+def scale_tensor(
+    dat: Num[Tensor, "... D"], inp_scale: ValidScale, tgt_scale: ValidScale
+):
+    if inp_scale is None:
+        inp_scale = (0, 1)
+    if tgt_scale is None:
+        tgt_scale = (0, 1)
+    if isinstance(tgt_scale, Tensor):
+        assert dat.shape[-1] == tgt_scale.shape[-1]
+    dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])
+    dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]
+    return dat
+
+
+class _TruncExp(Function):  # pylint: disable=abstract-method
+    # Implementation from torch-ngp:
+    # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(ctx, x):  # pylint: disable=arguments-differ
+        ctx.save_for_backward(x)
+        return torch.exp(x)
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, g):  # pylint: disable=arguments-differ
+        x = ctx.saved_tensors[0]
+        return g * torch.exp(torch.clamp(x, max=15))
+
+
+trunc_exp = _TruncExp.apply
+
+
+def get_activation(name) -> Callable:
+    if name is None:
+        return lambda x: x
+    name = name.lower()
+    if name == "none":
+        return lambda x: x
+    elif name == "lin2srgb":
+        return lambda x: torch.where(
+            x > 0.0031308,
+            torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055,
+            12.92 * x,
+        ).clamp(0.0, 1.0)
+    elif name == "exp":
+        return lambda x: torch.exp(x)
+    elif name == "shifted_exp":
+        return lambda x: torch.exp(x - 1.0)
+    elif name == "trunc_exp":
+        return trunc_exp
+    elif name == "shifted_trunc_exp":
+        return lambda x: trunc_exp(x - 1.0)
+    elif name == "sigmoid":
+        return lambda x: torch.sigmoid(x)
+    elif name == "tanh":
+        return lambda x: torch.tanh(x)
+    elif name == "shifted_softplus":
+        return lambda x: F.softplus(x - 1.0)
+    elif name == "scale_-11_01":
+        return lambda x: x * 0.5 + 0.5
+    elif name == "negative":
+        return lambda x: -x
+    else:
+        try:
+            return getattr(F, name)
+        except AttributeError:
+            raise ValueError(f"Unknown activation function: {name}")
+
+
+def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any:
+    if chunk_size <= 0:
+        return func(*args, **kwargs)
+    B = None
+    for arg in list(args) + list(kwargs.values()):
+        if isinstance(arg, torch.Tensor):
+            B = arg.shape[0]
+            break
+    assert (
+        B is not None
+    ), "No tensor found in args or kwargs, cannot determine batch size."
+    out = defaultdict(list)
+    out_type = None
+    # max(1, B) to support B == 0
+    for i in range(0, max(1, B), chunk_size):
+        out_chunk = func(
+            *[
+                arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
+                for arg in args
+            ],
+            **{
+                k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
+                for k, arg in kwargs.items()
+            },
+        )
+        if out_chunk is None:
+            continue
+        out_type = type(out_chunk)
+        if isinstance(out_chunk, torch.Tensor):
+            out_chunk = {0: out_chunk}
+        elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list):
+            chunk_length = len(out_chunk)
+            out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)}
+        elif isinstance(out_chunk, dict):
+            pass
+        else:
+            print(
+                f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}."
+            )
+            exit(1)
+        for k, v in out_chunk.items():
+            v = v if torch.is_grad_enabled() else v.detach()
+            out[k].append(v)
+
+    if out_type is None:
+        return None
+
+    out_merged: Dict[Any, Optional[torch.Tensor]] = {}
+    for k, v in out.items():
+        if all([vv is None for vv in v]):
+            # allow None in return value
+            out_merged[k] = None
+        elif all([isinstance(vv, torch.Tensor) for vv in v]):
+            out_merged[k] = torch.cat(v, dim=0)
+        else:
+            raise TypeError(
+                f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}"
+            )
+
+    if out_type is torch.Tensor:
+        return out_merged[0]
+    elif out_type in [tuple, list]:
+        return out_type([out_merged[i] for i in range(chunk_length)])
+    elif out_type is dict:
+        return out_merged
+
+
+def get_ray_directions(
+    H: int,
+    W: int,
+    focal: Union[float, Tuple[float, float]],
+    principal: Optional[Tuple[float, float]] = None,
+    use_pixel_centers: bool = True,
+    normalize: bool = True,
+) -> Float[Tensor, "H W 3"]:
+    """
+    Get ray directions for all pixels in camera coordinate.
+    Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/
+               ray-tracing-generating-camera-rays/standard-coordinate-systems
+
+    Inputs:
+        H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers
+    Outputs:
+        directions: (H, W, 3), the direction of the rays in camera coordinate
+    """
+    pixel_center = 0.5 if use_pixel_centers else 0
+
+    if isinstance(focal, float):
+        fx, fy = focal, focal
+        cx, cy = W / 2, H / 2
+    else:
+        fx, fy = focal
+        assert principal is not None
+        cx, cy = principal
+
+    i, j = torch.meshgrid(
+        torch.arange(W, dtype=torch.float32) + pixel_center,
+        torch.arange(H, dtype=torch.float32) + pixel_center,
+        indexing="xy",
+    )
+
+    directions: Float[Tensor, "H W 3"] = torch.stack(
+        [(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1
+    )
+
+    if normalize:
+        directions = F.normalize(directions, dim=-1)
+
+    return directions
+
+
+def get_rays(
+    directions: Float[Tensor, "... 3"],
+    c2w: Float[Tensor, "... 4 4"],
+    keepdim=False,
+    noise_scale=0.0,
+    normalize=False,
+) -> Tuple[Float[Tensor, "... 3"], Float[Tensor, "... 3"]]:
+    # Rotate ray directions from camera coordinate to the world coordinate
+    assert directions.shape[-1] == 3
+
+    if directions.ndim == 2:  # (N_rays, 3)
+        if c2w.ndim == 2:  # (4, 4)
+            c2w = c2w[None, :, :]
+        assert c2w.ndim == 3  # (N_rays, 4, 4) or (1, 4, 4)
+        rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1)  # (N_rays, 3)
+        rays_o = c2w[:, :3, 3].expand(rays_d.shape)
+    elif directions.ndim == 3:  # (H, W, 3)
+        assert c2w.ndim in [2, 3]
+        if c2w.ndim == 2:  # (4, 4)
+            rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum(
+                -1
+            )  # (H, W, 3)
+            rays_o = c2w[None, None, :3, 3].expand(rays_d.shape)
+        elif c2w.ndim == 3:  # (B, 4, 4)
+            rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
+                -1
+            )  # (B, H, W, 3)
+            rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
+    elif directions.ndim == 4:  # (B, H, W, 3)
+        assert c2w.ndim == 3  # (B, 4, 4)
+        rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
+            -1
+        )  # (B, H, W, 3)
+        rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
+
+    # add camera noise to avoid grid-like artifect
+    # https://github.com/ashawkey/stable-dreamfusion/blob/49c3d4fa01d68a4f027755acf94e1ff6020458cc/nerf/utils.py#L373
+    if noise_scale > 0:
+        rays_o = rays_o + torch.randn(3, device=rays_o.device) * noise_scale
+        rays_d = rays_d + torch.randn(3, device=rays_d.device) * noise_scale
+
+    if normalize:
+        rays_d = F.normalize(rays_d, dim=-1)
+    if not keepdim:
+        rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)
+
+    return rays_o, rays_d
+
+
+def get_projection_matrix(
+    fovy: Union[float, Float[Tensor, "B"]], aspect_wh: float, near: float, far: float
+) -> Float[Tensor, "*B 4 4"]:
+    if isinstance(fovy, float):
+        proj_mtx = torch.zeros(4, 4, dtype=torch.float32)
+        proj_mtx[0, 0] = 1.0 / (math.tan(fovy / 2.0) * aspect_wh)
+        proj_mtx[1, 1] = -1.0 / math.tan(
+            fovy / 2.0
+        )  # add a negative sign here as the y axis is flipped in nvdiffrast output
+        proj_mtx[2, 2] = -(far + near) / (far - near)
+        proj_mtx[2, 3] = -2.0 * far * near / (far - near)
+        proj_mtx[3, 2] = -1.0
+    else:
+        batch_size = fovy.shape[0]
+        proj_mtx = torch.zeros(batch_size, 4, 4, dtype=torch.float32)
+        proj_mtx[:, 0, 0] = 1.0 / (torch.tan(fovy / 2.0) * aspect_wh)
+        proj_mtx[:, 1, 1] = -1.0 / torch.tan(
+            fovy / 2.0
+        )  # add a negative sign here as the y axis is flipped in nvdiffrast output
+        proj_mtx[:, 2, 2] = -(far + near) / (far - near)
+        proj_mtx[:, 2, 3] = -2.0 * far * near / (far - near)
+        proj_mtx[:, 3, 2] = -1.0
+    return proj_mtx
+
+
+def get_mvp_matrix(
+    c2w: Float[Tensor, "*B 4 4"], proj_mtx: Float[Tensor, "*B 4 4"]
+) -> Float[Tensor, "*B 4 4"]:
+    # calculate w2c from c2w: R' = Rt, t' = -Rt * t
+    # mathematically equivalent to (c2w)^-1
+    if c2w.ndim == 2:
+        assert proj_mtx.ndim == 2
+        w2c: Float[Tensor, "4 4"] = torch.zeros(4, 4).to(c2w)
+        w2c[:3, :3] = c2w[:3, :3].permute(1, 0)
+        w2c[:3, 3:] = -c2w[:3, :3].permute(1, 0) @ c2w[:3, 3:]
+        w2c[3, 3] = 1.0
+    else:
+        w2c: Float[Tensor, "B 4 4"] = torch.zeros(c2w.shape[0], 4, 4).to(c2w)
+        w2c[:, :3, :3] = c2w[:, :3, :3].permute(0, 2, 1)
+        w2c[:, :3, 3:] = -c2w[:, :3, :3].permute(0, 2, 1) @ c2w[:, :3, 3:]
+        w2c[:, 3, 3] = 1.0
+    # calculate mvp matrix by proj_mtx @ w2c (mv_mtx)
+    mvp_mtx = proj_mtx @ w2c
+    return mvp_mtx
+
+
+def get_intrinsic_from_fov(fov, H, W, bs=-1):
+    focal_length = 0.5 * H / np.tan(0.5 * fov)
+    intrinsic = np.identity(3, dtype=np.float32)
+    intrinsic[0, 0] = focal_length
+    intrinsic[1, 1] = focal_length
+    intrinsic[0, 2] = W / 2.0
+    intrinsic[1, 2] = H / 2.0
+
+    if bs > 0:
+        intrinsic = intrinsic[None].repeat(bs, axis=0)
+
+    return torch.from_numpy(intrinsic)
+
+
+def binary_cross_entropy(input, target):
+    """
+    F.binary_cross_entropy is not numerically stable in mixed-precision training.
+    """
+    return -(target * torch.log(input) + (1 - target) * torch.log(1 - input)).mean()
+
+
+def tet_sdf_diff(
+    vert_sdf: Float[Tensor, "Nv 1"], tet_edges: Integer[Tensor, "Ne 2"]
+) -> Float[Tensor, ""]:
+    sdf_f1x6x2 = vert_sdf[:, 0][tet_edges.reshape(-1)].reshape(-1, 2)
+    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
+    sdf_f1x6x2 = sdf_f1x6x2[mask]
+    sdf_diff = F.binary_cross_entropy_with_logits(
+        sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()
+    ) + F.binary_cross_entropy_with_logits(
+        sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float()
+    )
+    return sdf_diff
+
+
+def validate_empty_rays(ray_indices, t_start, t_end):
+    if ray_indices.nelement() == 0:
+        warn("Empty rays_indices!")
+        ray_indices = torch.LongTensor([0]).to(ray_indices)
+        t_start = torch.Tensor([0]).to(ray_indices)
+        t_end = torch.Tensor([0]).to(ray_indices)
+    return ray_indices, t_start, t_end
+
+
+def rays_intersect_bbox(
+    rays_o: Float[Tensor, "N 3"],
+    rays_d: Float[Tensor, "N 3"],
+    radius: Float,
+    near: Float = 0.0,
+    valid_thresh: Float = 0.01,
+    background: bool = False,
+):
+    input_shape = rays_o.shape[:-1]
+    rays_o, rays_d = rays_o.view(-1, 3), rays_d.view(-1, 3)
+    rays_d_valid = torch.where(
+        rays_d.abs() < 1e-6, torch.full_like(rays_d, 1e-6), rays_d
+    )
+    if type(radius) in [int, float]:
+        radius = torch.FloatTensor(
+            [[-radius, radius], [-radius, radius], [-radius, radius]]
+        ).to(rays_o.device)
+    radius = (
+        1.0 - 1.0e-3
+    ) * radius  # tighten the radius to make sure the intersection point lies in the bounding box
+    interx0 = (radius[..., 1] - rays_o) / rays_d_valid
+    interx1 = (radius[..., 0] - rays_o) / rays_d_valid
+    t_near = torch.minimum(interx0, interx1).amax(dim=-1).clamp_min(near)
+    t_far = torch.maximum(interx0, interx1).amin(dim=-1)
+
+    # check wheter a ray intersects the bbox or not
+    rays_valid = t_far - t_near > valid_thresh
+
+    t_near_valid, t_far_valid = t_near[rays_valid], t_far[rays_valid]
+    global_near = t_near_valid.min().item()
+    global_far = t_far_valid.max().item()
+
+    t_near[torch.where(~rays_valid)] = 0.0
+    t_far[torch.where(~rays_valid)] = 0.0
+
+    t_near = t_near.view(*input_shape, 1)
+    t_far = t_far.view(*input_shape, 1)
+    rays_valid = rays_valid.view(*input_shape)
+
+    return t_near, t_far, rays_valid
+
+
+def get_plucker_rays(
+    rays_o: Float[Tensor, "*N 3"], rays_d: Float[Tensor, "*N 3"]
+) -> Float[Tensor, "*N 6"]:
+    rays_o = F.normalize(rays_o, dim=-1)
+    rays_d = F.normalize(rays_d, dim=-1)
+    return torch.cat([rays_o.cross(rays_d), rays_d], dim=-1)
+
+
+def c2w_to_polar(c2w: Float[Tensor, "4 4"]) -> Tuple[float, float, float]:
+    cam_pos = c2w[:3, 3]
+    x, y, z = cam_pos.tolist()
+    distance = cam_pos.norm().item()
+    elevation = math.asin(z / distance)
+    if abs(x) < 1.0e-5 and abs(y) < 1.0e-5:
+        azimuth = 0
+    else:
+        azimuth = math.atan2(y, x)
+        if azimuth < 0:
+            azimuth += 2 * math.pi
+
+    return elevation, azimuth, distance
+
+
+def polar_to_c2w(
+    elevation: float, azimuth: float, distance: float
+) -> Float[Tensor, "4 4"]:
+    """
+    Compute L = p - C.
+    Normalize L.
+    Compute s = L x u. (cross product)
+    Normalize s.
+    Compute u' = s x L.
+    rotation = [s, u, -l]
+    """
+    z = distance * math.sin(elevation)
+    x = distance * math.cos(elevation) * math.cos(azimuth)
+    y = distance * math.cos(elevation) * math.sin(azimuth)
+    l = -torch.as_tensor([x, y, z]).float()
+    l = F.normalize(l, dim=0)
+    u = torch.as_tensor([0.0, 0.0, 1.0]).float()
+    s = l.cross(u)
+    s = F.normalize(s, dim=0)
+    u = s.cross(l)
+    rot = torch.stack([s, u, -l], dim=0).T
+    c2w = torch.zeros((4, 4), dtype=torch.float32)
+    c2w[:3, :3] = rot
+    c2w[:3, 3] = torch.as_tensor([x, y, z])
+    c2w[3, 3] = 1
+    return c2w
+
+
+def fourier_position_encoding(x, n_freq: int, dim: int):
+    assert n_freq > 0
+    input_shape = x.shape
+    input_ndim = x.ndim
+    if dim < 0:
+        dim = input_ndim + dim
+    bands = 2 ** torch.arange(n_freq, dtype=x.dtype, device=x.device)
+    for i in range(dim + 1):
+        bands = bands.unsqueeze(0)
+    for i in range(input_ndim - dim - 1):
+        bands = bands.unsqueeze(-1)
+    x = x.view(*input_shape[: dim + 1], 1, *input_shape[dim + 1 :])
+    x = torch.cat(
+        [
+            torch.sin(bands * x).reshape(
+                *input_shape[:dim], -1, *input_shape[dim + 1 :]
+            ),
+            torch.cos(bands * x).reshape(
+                *input_shape[:dim], -1, *input_shape[dim + 1 :]
+            ),
+        ],
+        dim=dim,
+    )
+    return x
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/saving.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/saving.py
new file mode 100644
index 0000000..3282772
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/saving.py
@@ -0,0 +1,544 @@
+import json
+import math
+import os
+import re
+import shutil
+from typing import List, Optional, Union
+
+import cv2
+import imageio
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+# import wandb
+from matplotlib import cm
+from matplotlib.colors import LinearSegmentedColormap
+from PIL import Image, ImageDraw
+
+from .typing import *
+
+
+def image_to_tensor(
+    images: List[Image.Image], device: str = "cuda"
+) -> torch.FloatTensor:
+    tensors = []
+    for image in images:
+        tensor = torch.from_numpy(np.array(image)).float() / 255.0
+        tensor = tensor.to(device)
+        tensors.append(tensor)
+    return torch.stack(tensors)
+
+
+def tensor_to_image(
+    data: Union[Image.Image, torch.Tensor, np.ndarray],
+    batched: bool = False,
+    format: str = "HWC",
+) -> Union[Image.Image, List[Image.Image]]:
+    if isinstance(data, Image.Image):
+        return data
+    if isinstance(data, torch.Tensor):
+        data = data.detach().cpu().numpy()
+    if data.dtype == np.float32 or data.dtype == np.float16:
+        data = (data * 255).astype(np.uint8)
+    elif data.dtype == np.bool_:
+        data = data.astype(np.uint8) * 255
+    assert data.dtype == np.uint8
+    if format == "CHW":
+        if batched and data.ndim == 4:
+            data = data.transpose((0, 2, 3, 1))
+        elif not batched and data.ndim == 3:
+            data = data.transpose((1, 2, 0))
+
+    if batched:
+        return [Image.fromarray(d) for d in data]
+    return Image.fromarray(data)
+
+
+def largest_factor_near_sqrt(n: int) -> int:
+    """
+    Finds the largest factor of n that is closest to the square root of n.
+
+    Args:
+        n (int): The integer for which to find the largest factor near its square root.
+
+    Returns:
+        int: The largest factor of n that is closest to the square root of n.
+    """
+    sqrt_n = int(math.sqrt(n))  # Get the integer part of the square root
+
+    # First, check if the square root itself is a factor
+    if sqrt_n * sqrt_n == n:
+        return sqrt_n
+
+    # Otherwise, find the largest factor by iterating from sqrt_n downwards
+    for i in range(sqrt_n, 0, -1):
+        if n % i == 0:
+            return i
+
+    # If n is 1, return 1
+    return 1
+
+
+def make_image_grid(
+    images: List[Image.Image],
+    rows: Optional[int] = None,
+    cols: Optional[int] = None,
+    resize: Optional[int] = None,
+) -> Image.Image:
+    """
+    Prepares a single grid of images. Useful for visualization purposes.
+    """
+    if rows is None and cols is not None:
+        assert len(images) % cols == 0
+        rows = len(images) // cols
+    elif cols is None and rows is not None:
+        assert len(images) % rows == 0
+        cols = len(images) // rows
+    elif rows is None and cols is None:
+        rows = largest_factor_near_sqrt(len(images))
+        cols = len(images) // rows
+
+    assert len(images) == rows * cols
+
+    if resize is not None:
+        images = [img.resize((resize, resize)) for img in images]
+
+    w, h = images[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+
+    for i, img in enumerate(images):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+class SaverMixin:
+    _save_dir: Optional[str] = None
+    _wandb_logger: Optional[Any] = None
+
+    def set_save_dir(self, save_dir: str):
+        self._save_dir = save_dir
+
+    def get_save_dir(self):
+        if self._save_dir is None:
+            raise ValueError("Save dir is not set")
+        return self._save_dir
+
+    def convert_data(self, data):
+        if data is None:
+            return None
+        elif isinstance(data, np.ndarray):
+            return data
+        elif isinstance(data, torch.Tensor):
+            if data.dtype in [torch.float16, torch.bfloat16]:
+                data = data.float()
+            return data.detach().cpu().numpy()
+        elif isinstance(data, list):
+            return [self.convert_data(d) for d in data]
+        elif isinstance(data, dict):
+            return {k: self.convert_data(v) for k, v in data.items()}
+        else:
+            raise TypeError(
+                "Data must be in type numpy.ndarray, torch.Tensor, list or dict, getting",
+                type(data),
+            )
+
+    def get_save_path(self, filename):
+        save_path = os.path.join(self.get_save_dir(), filename)
+        os.makedirs(os.path.dirname(save_path), exist_ok=True)
+        return save_path
+
+    DEFAULT_RGB_KWARGS = {"data_format": "HWC", "data_range": (0, 1)}
+    DEFAULT_UV_KWARGS = {
+        "data_format": "HWC",
+        "data_range": (0, 1),
+        "cmap": "checkerboard",
+    }
+    DEFAULT_GRAYSCALE_KWARGS = {"data_range": None, "cmap": "jet"}
+    DEFAULT_GRID_KWARGS = {"align": "max"}
+
+    def get_rgb_image_(self, img, data_format, data_range, rgba=False):
+        img = self.convert_data(img)
+        assert data_format in ["CHW", "HWC"]
+        if data_format == "CHW":
+            img = img.transpose(1, 2, 0)
+        if img.dtype != np.uint8:
+            img = img.clip(min=data_range[0], max=data_range[1])
+            img = (
+                (img - data_range[0]) / (data_range[1] - data_range[0]) * 255.0
+            ).astype(np.uint8)
+        nc = 4 if rgba else 3
+        imgs = [img[..., start : start + nc] for start in range(0, img.shape[-1], nc)]
+        imgs = [
+            (
+                img_
+                if img_.shape[-1] == nc
+                else np.concatenate(
+                    [
+                        img_,
+                        np.zeros(
+                            (img_.shape[0], img_.shape[1], nc - img_.shape[2]),
+                            dtype=img_.dtype,
+                        ),
+                    ],
+                    axis=-1,
+                )
+            )
+            for img_ in imgs
+        ]
+        img = np.concatenate(imgs, axis=1)
+        if rgba:
+            img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
+        else:
+            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        return img
+
+    def _save_rgb_image(
+        self,
+        filename,
+        img,
+        data_format,
+        data_range,
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+    ):
+        img = self.get_rgb_image_(img, data_format, data_range)
+        cv2.imwrite(filename, img)
+        if name and self._wandb_logger:
+            self._wandb_logger.log_image(
+                key=name, images=[self.get_save_path(filename)], step=step
+            )
+
+    def save_rgb_image(
+        self,
+        filename,
+        img,
+        data_format=DEFAULT_RGB_KWARGS["data_format"],
+        data_range=DEFAULT_RGB_KWARGS["data_range"],
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+    ) -> str:
+        save_path = self.get_save_path(filename)
+        self._save_rgb_image(save_path, img, data_format, data_range, name, step)
+        return save_path
+
+    def get_uv_image_(self, img, data_format, data_range, cmap):
+        img = self.convert_data(img)
+        assert data_format in ["CHW", "HWC"]
+        if data_format == "CHW":
+            img = img.transpose(1, 2, 0)
+        img = img.clip(min=data_range[0], max=data_range[1])
+        img = (img - data_range[0]) / (data_range[1] - data_range[0])
+        assert cmap in ["checkerboard", "color"]
+        if cmap == "checkerboard":
+            n_grid = 64
+            mask = (img * n_grid).astype(int)
+            mask = (mask[..., 0] + mask[..., 1]) % 2 == 0
+            img = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 255
+            img[mask] = np.array([255, 0, 255], dtype=np.uint8)
+            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        elif cmap == "color":
+            img_ = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
+            img_[..., 0] = (img[..., 0] * 255).astype(np.uint8)
+            img_[..., 1] = (img[..., 1] * 255).astype(np.uint8)
+            img_ = cv2.cvtColor(img_, cv2.COLOR_RGB2BGR)
+            img = img_
+        return img
+
+    def save_uv_image(
+        self,
+        filename,
+        img,
+        data_format=DEFAULT_UV_KWARGS["data_format"],
+        data_range=DEFAULT_UV_KWARGS["data_range"],
+        cmap=DEFAULT_UV_KWARGS["cmap"],
+    ) -> str:
+        save_path = self.get_save_path(filename)
+        img = self.get_uv_image_(img, data_format, data_range, cmap)
+        cv2.imwrite(save_path, img)
+        return save_path
+
+    def get_grayscale_image_(self, img, data_range, cmap):
+        img = self.convert_data(img)
+        img = np.nan_to_num(img)
+        if data_range is None:
+            img = (img - img.min()) / (img.max() - img.min())
+        else:
+            img = img.clip(data_range[0], data_range[1])
+            img = (img - data_range[0]) / (data_range[1] - data_range[0])
+        assert cmap in [None, "jet", "magma", "spectral"]
+        if cmap == None:
+            img = (img * 255.0).astype(np.uint8)
+            img = np.repeat(img[..., None], 3, axis=2)
+        elif cmap == "jet":
+            img = (img * 255.0).astype(np.uint8)
+            img = cv2.applyColorMap(img, cv2.COLORMAP_JET)
+        elif cmap == "magma":
+            img = 1.0 - img
+            base = cm.get_cmap("magma")
+            num_bins = 256
+            colormap = LinearSegmentedColormap.from_list(
+                f"{base.name}{num_bins}", base(np.linspace(0, 1, num_bins)), num_bins
+            )(np.linspace(0, 1, num_bins))[:, :3]
+            a = np.floor(img * 255.0)
+            b = (a + 1).clip(max=255.0)
+            f = img * 255.0 - a
+            a = a.astype(np.uint16).clip(0, 255)
+            b = b.astype(np.uint16).clip(0, 255)
+            img = colormap[a] + (colormap[b] - colormap[a]) * f[..., None]
+            img = (img * 255.0).astype(np.uint8)
+        elif cmap == "spectral":
+            colormap = plt.get_cmap("Spectral")
+
+            def blend_rgba(image):
+                image = image[..., :3] * image[..., -1:] + (
+                    1.0 - image[..., -1:]
+                )  # blend A to RGB
+                return image
+
+            img = colormap(img)
+            img = blend_rgba(img)
+            img = (img * 255).astype(np.uint8)
+            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        return img
+
+    def _save_grayscale_image(
+        self,
+        filename,
+        img,
+        data_range,
+        cmap,
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+    ):
+        img = self.get_grayscale_image_(img, data_range, cmap)
+        cv2.imwrite(filename, img)
+        if name and self._wandb_logger:
+            self._wandb_logger.log_image(
+                key=name, images=[self.get_save_path(filename)], step=step
+            )
+
+    def save_grayscale_image(
+        self,
+        filename,
+        img,
+        data_range=DEFAULT_GRAYSCALE_KWARGS["data_range"],
+        cmap=DEFAULT_GRAYSCALE_KWARGS["cmap"],
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+    ) -> str:
+        save_path = self.get_save_path(filename)
+        self._save_grayscale_image(save_path, img, data_range, cmap, name, step)
+        return save_path
+
+    def get_image_grid_(self, imgs, align):
+        if isinstance(imgs[0], list):
+            return np.concatenate(
+                [self.get_image_grid_(row, align) for row in imgs], axis=0
+            )
+        cols = []
+        for col in imgs:
+            assert col["type"] in ["rgb", "uv", "grayscale"]
+            if col["type"] == "rgb":
+                rgb_kwargs = self.DEFAULT_RGB_KWARGS.copy()
+                rgb_kwargs.update(col["kwargs"])
+                cols.append(self.get_rgb_image_(col["img"], **rgb_kwargs))
+            elif col["type"] == "uv":
+                uv_kwargs = self.DEFAULT_UV_KWARGS.copy()
+                uv_kwargs.update(col["kwargs"])
+                cols.append(self.get_uv_image_(col["img"], **uv_kwargs))
+            elif col["type"] == "grayscale":
+                grayscale_kwargs = self.DEFAULT_GRAYSCALE_KWARGS.copy()
+                grayscale_kwargs.update(col["kwargs"])
+                cols.append(self.get_grayscale_image_(col["img"], **grayscale_kwargs))
+
+        if align == "max":
+            h = max([col.shape[0] for col in cols])
+        elif align == "min":
+            h = min([col.shape[0] for col in cols])
+        elif isinstance(align, int):
+            h = align
+        else:
+            raise ValueError(
+                f"Unsupported image grid align: {align}, should be min, max, or int"
+            )
+
+        for i in range(len(cols)):
+            if cols[i].shape[0] != h:
+                w = int(cols[i].shape[1] * h / cols[i].shape[0])
+                cols[i] = cv2.resize(cols[i], (w, h), interpolation=cv2.INTER_CUBIC)
+        return np.concatenate(cols, axis=1)
+
+    def save_image_grid(
+        self,
+        filename,
+        imgs,
+        align=DEFAULT_GRID_KWARGS["align"],
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+        texts: Optional[List[float]] = None,
+    ):
+        save_path = self.get_save_path(filename)
+        img = self.get_image_grid_(imgs, align=align)
+
+        if texts is not None:
+            img = Image.fromarray(img)
+            draw = ImageDraw.Draw(img)
+            black, white = (0, 0, 0), (255, 255, 255)
+            for i, text in enumerate(texts):
+                draw.text((2, (img.size[1] // len(texts)) * i + 1), f"{text}", white)
+                draw.text((0, (img.size[1] // len(texts)) * i + 1), f"{text}", white)
+                draw.text((2, (img.size[1] // len(texts)) * i - 1), f"{text}", white)
+                draw.text((0, (img.size[1] // len(texts)) * i - 1), f"{text}", white)
+                draw.text((1, (img.size[1] // len(texts)) * i), f"{text}", black)
+            img = np.asarray(img)
+
+        cv2.imwrite(save_path, img)
+        if name and self._wandb_logger:
+            self._wandb_logger.log_image(key=name, images=[save_path], step=step)
+        return save_path
+
+    def save_image(self, filename, img) -> str:
+        save_path = self.get_save_path(filename)
+        img = self.convert_data(img)
+        assert img.dtype == np.uint8 or img.dtype == np.uint16
+        if img.ndim == 3 and img.shape[-1] == 3:
+            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        elif img.ndim == 3 and img.shape[-1] == 4:
+            img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
+        cv2.imwrite(save_path, img)
+        return save_path
+
+    def save_cubemap(self, filename, img, data_range=(0, 1), rgba=False) -> str:
+        save_path = self.get_save_path(filename)
+        img = self.convert_data(img)
+        assert img.ndim == 4 and img.shape[0] == 6 and img.shape[1] == img.shape[2]
+
+        imgs_full = []
+        for start in range(0, img.shape[-1], 3):
+            img_ = img[..., start : start + 3]
+            img_ = np.stack(
+                [
+                    self.get_rgb_image_(img_[i], "HWC", data_range, rgba=rgba)
+                    for i in range(img_.shape[0])
+                ],
+                axis=0,
+            )
+            size = img_.shape[1]
+            placeholder = np.zeros((size, size, 3), dtype=np.float32)
+            img_full = np.concatenate(
+                [
+                    np.concatenate(
+                        [placeholder, img_[2], placeholder, placeholder], axis=1
+                    ),
+                    np.concatenate([img_[1], img_[4], img_[0], img_[5]], axis=1),
+                    np.concatenate(
+                        [placeholder, img_[3], placeholder, placeholder], axis=1
+                    ),
+                ],
+                axis=0,
+            )
+            imgs_full.append(img_full)
+
+        imgs_full = np.concatenate(imgs_full, axis=1)
+        cv2.imwrite(save_path, imgs_full)
+        return save_path
+
+    def save_data(self, filename, data) -> str:
+        data = self.convert_data(data)
+        if isinstance(data, dict):
+            if not filename.endswith(".npz"):
+                filename += ".npz"
+            save_path = self.get_save_path(filename)
+            np.savez(save_path, **data)
+        else:
+            if not filename.endswith(".npy"):
+                filename += ".npy"
+            save_path = self.get_save_path(filename)
+            np.save(save_path, data)
+        return save_path
+
+    def save_state_dict(self, filename, data) -> str:
+        save_path = self.get_save_path(filename)
+        torch.save(data, save_path)
+        return save_path
+
+    def save_img_sequence(
+        self,
+        filename,
+        img_dir,
+        matcher,
+        save_format="mp4",
+        fps=30,
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+    ) -> str:
+        assert save_format in ["gif", "mp4"]
+        if not filename.endswith(save_format):
+            filename += f".{save_format}"
+        save_path = self.get_save_path(filename)
+        matcher = re.compile(matcher)
+        img_dir = os.path.join(self.get_save_dir(), img_dir)
+        imgs = []
+        for f in os.listdir(img_dir):
+            if matcher.search(f):
+                imgs.append(f)
+        imgs = sorted(imgs, key=lambda f: int(matcher.search(f).groups()[0]))
+        imgs = [cv2.imread(os.path.join(img_dir, f)) for f in imgs]
+
+        if save_format == "gif":
+            imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs]
+            imageio.mimsave(save_path, imgs, fps=fps, palettesize=256)
+        elif save_format == "mp4":
+            imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs]
+            imageio.mimsave(save_path, imgs, fps=fps)
+        if name and self._wandb_logger:
+            from .core import warn
+
+            warn("Wandb logger does not support video logging yet!")
+        return save_path
+
+    def save_img_sequences(
+        self,
+        seq_dir,
+        matcher,
+        save_format="mp4",
+        fps=30,
+        delete=True,
+        name: Optional[str] = None,
+        step: Optional[int] = None,
+    ):
+        seq_dir_ = os.path.join(self.get_save_dir(), seq_dir)
+        for f in os.listdir(seq_dir_):
+            img_dir_ = os.path.join(seq_dir_, f)
+            if not os.path.isdir(img_dir_):
+                continue
+            try:
+                self.save_img_sequence(
+                    os.path.join(seq_dir, f),
+                    os.path.join(seq_dir, f),
+                    matcher,
+                    save_format=save_format,
+                    fps=fps,
+                    name=f"{name}_{f}",
+                    step=step,
+                )
+                if delete:
+                    shutil.rmtree(img_dir_)
+            except:
+                from .core import warn
+
+                warn(f"Video saving for directory {seq_dir_} failed!")
+
+    def save_file(self, filename, src_path, delete=False) -> str:
+        save_path = self.get_save_path(filename)
+        shutil.copyfile(src_path, save_path)
+        if delete:
+            os.remove(src_path)
+        return save_path
+
+    def save_json(self, filename, payload) -> str:
+        save_path = self.get_save_path(filename)
+        with open(save_path, "w") as f:
+            f.write(json.dumps(payload))
+        return save_path
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter/utils/typing.py b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/typing.py
new file mode 100644
index 0000000..d4bb88e
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter/utils/typing.py
@@ -0,0 +1,40 @@
+"""
+This module contains type annotations for the project, using
+1. Python type hints (https://docs.python.org/3/library/typing.html) for Python objects
+2. jaxtyping (https://github.com/google/jaxtyping/blob/main/API.md) for PyTorch tensors
+
+Two types of typing checking can be used:
+1. Static type checking with mypy (install with pip and enabled as the default linter in VSCode)
+2. Runtime type checking with typeguard (install with pip and triggered at runtime, mainly for tensor dtype and shape checking)
+"""
+
+# Basic types
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Literal,
+    NamedTuple,
+    NewType,
+    Optional,
+    Sized,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+
+# Tensor dtype
+# for jaxtyping usage, see https://github.com/google/jaxtyping/blob/main/API.md
+from jaxtyping import Bool, Complex, Float, Inexact, Int, Integer, Num, Shaped, UInt
+
+# Config type
+from omegaconf import DictConfig, ListConfig
+
+# PyTorch Tensor type
+from torch import Tensor
+
+# Runtime type checking decorator
+from typeguard import typechecked as typechecker
diff --git a/Gen_3D_Modules/MV_Adapter/mvadapter_node_utils.py b/Gen_3D_Modules/MV_Adapter/mvadapter_node_utils.py
new file mode 100644
index 0000000..3a98854
--- /dev/null
+++ b/Gen_3D_Modules/MV_Adapter/mvadapter_node_utils.py
@@ -0,0 +1,661 @@
+"""
+Utilities for ComfyUI MV-Adapter nodes
+"""
+
+import os
+import torch
+import numpy as np
+from PIL import Image
+from typing import Optional, Union, List
+from torchvision import transforms
+from transformers import AutoModelForImageSegmentation
+
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+from huggingface_hub import snapshot_download
+
+from .mvadapter.pipelines.pipeline_mvadapter_i2mv_sdxl import MVAdapterI2MVSDXLPipeline
+from .mvadapter.pipelines.pipeline_mvadapter_t2mv_sdxl import MVAdapterT2MVSDXLPipeline
+from .mvadapter.models.attention_processor import DecoupledMVRowColSelfAttnProcessor2_0
+from .mvadapter.schedulers.scheduling_shift_snr import ShiftSNRScheduler
+from .mvadapter.utils.mesh_utils import (
+    NVDiffRastContextWrapper,
+    get_orthogonal_camera,
+    load_mesh,
+    render,
+)
+from .mvadapter.utils import make_image_grid, tensor_to_image
+from .mvadapter.utils.geometry import get_plucker_embeds_from_cameras_ortho
+
+try:
+    from mmgp import offload, profile_type
+    MMGP_AVAILABLE = True
+except ImportError:
+    MMGP_AVAILABLE = False
+    print("Warning: mmgp not available, memory optimization disabled")
+
+
+def prepare_pipeline(
+    base_model: str,
+    vae_model: Optional[str] = None,
+    lora_model: Optional[str] = None,
+    adapter_path: str = "huanngzh/mv-adapter",
+    scheduler: str = "ddpm",
+    num_views: int = 6,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.float16,
+    use_mmgp: bool = False,
+    adapter_local_path: Optional[str] = None,
+):
+    """
+    Prepare MV-Adapter pipeline
+    
+    Args:
+        base_model: Base Stable Diffusion XL model
+        vae_model: Custom VAE model (optional)
+        lora_model: LoRA model (optional)
+        adapter_path: Path to adapter weights
+        scheduler: Scheduler type ("ddpm")
+        num_views: Number of views to generate
+        device: Device for computation
+        dtype: Data type
+        use_mmgp: Use mmgp for memory optimization
+        adapter_local_path: Local path for adapter download
+    """
+    # Load vae if provided
+    pipe_kwargs = {}
+    if vae_model is not None:
+        pipe_kwargs["vae"] = AutoencoderKL.from_pretrained(vae_model)
+    
+    # Prepare pipeline
+    pipe = MVAdapterI2MVSDXLPipeline.from_pretrained(base_model, **pipe_kwargs)
+
+    # Load DDPM scheduler
+    pipe.scheduler = ShiftSNRScheduler.from_scheduler(
+        pipe.scheduler,
+        shift_mode="interpolated",
+        shift_scale=8.0,
+        scheduler_class=DDPMScheduler,
+    )
+    
+    pipe.init_custom_adapter(
+        num_views=num_views, self_attn_processor=DecoupledMVRowColSelfAttnProcessor2_0
+    )
+    
+    # Load adapter weights
+    weight_name = "mvadapter_ig2mv_sdxl.safetensors"
+    pipe.load_custom_adapter(adapter_path, weight_name=weight_name, local_cache_dir=adapter_local_path)
+
+    pipe.to(device=device, dtype=dtype)
+    pipe.cond_encoder.to(device=device, dtype=dtype)
+
+    # load lora if provided
+    if lora_model is not None:
+        model_, name_ = lora_model.rsplit("/", 1)
+        pipe.load_lora_weights(model_, weight_name=name_)
+
+    # Apply mmgp optimization if available and requested
+    if use_mmgp and MMGP_AVAILABLE:
+        import gc
+        torch.cuda.empty_cache()
+        gc.collect()
+        
+        all_models = {
+            "vae": pipe.vae,
+            "text_encoder": pipe.text_encoder,
+            "text_encoder_2": pipe.text_encoder_2,
+            "unet": pipe.unet,
+            "cond_encoder": pipe.cond_encoder,  
+        }
+        
+        if hasattr(pipe, 'image_encoder') and pipe.image_encoder is not None:
+            all_models["image_encoder"] = pipe.image_encoder
+        
+        offload.profile(all_models, profile_type.HighRAM_LowVRAM)
+        print("mmgp profiling applied successfully.")
+    
+    # Enable VAE slicing for memory efficiency
+    pipe.enable_vae_slicing()
+
+    return pipe
+
+
+def preprocess_image(image: Image.Image, height: int, width: int) -> Image.Image:
+    """Preprocess image for MV-Adapter"""
+    image = np.array(image)
+    alpha = image[..., 3] > 0
+    H, W = alpha.shape
+    # get the bounding box of alpha
+    y, x = np.where(alpha)
+    y0, y1 = max(y.min() - 1, 0), min(y.max() + 1, H)
+    x0, x1 = max(x.min() - 1, 0), min(x.max() + 1, W)
+    image_center = image[y0:y1, x0:x1]
+    # resize the longer side to H * 0.9
+    H, W, _ = image_center.shape
+    if H > W:
+        W = int(W * (height * 0.9) / H)
+        H = int(height * 0.9)
+    else:
+        H = int(H * (width * 0.9) / W)
+        W = int(width * 0.9)
+    image_center = np.array(Image.fromarray(image_center).resize((W, H)))
+    # pad to H, W
+    start_h = (height - H) // 2
+    start_w = (width - W) // 2
+    image = np.zeros((height, width, 4), dtype=np.uint8)
+    image[start_h : start_h + H, start_w : start_w + W] = image_center
+    image = image.astype(np.float32) / 255.0
+    image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
+    image = (image * 255).clip(0, 255).astype(np.uint8)
+    image = Image.fromarray(image)
+
+    return image
+
+
+def run_pipeline(
+    pipe,
+    mesh_path: str,
+    num_views: int,
+    text: str,
+    image: Union[str, Image.Image],
+    height: int,
+    width: int,
+    num_inference_steps: int,
+    guidance_scale: float,
+    seed: int,
+    remove_bg_fn=None,
+    reference_conditioning_scale: float = 1.0,
+    negative_prompt: str = "watermark, ugly, deformed, noisy, blurry, low contrast",
+    lora_scale: float = 1.0,
+    device: str = "cuda",
+):
+    """
+    Execute multi-view image generation
+    """
+    # Prepare cameras
+    cameras = get_orthogonal_camera(
+        elevation_deg=[0, 0, 0, 0, 89.99, -89.99],
+        distance=[1.8] * num_views,
+        left=-0.55,
+        right=0.55,
+        bottom=-0.55,
+        top=0.55,
+        azimuth_deg=[x - 90 for x in [0, 90, 180, 270, 180, 180]],
+        device=device,
+    )
+    ctx = NVDiffRastContextWrapper(device=device)
+
+    mesh = load_mesh(mesh_path, rescale=True, device=device)
+    render_out = render(
+        ctx,
+        mesh,
+        cameras,
+        height=height,
+        width=width,
+        render_attr=False,
+        normal_background=0.0,
+    )
+    
+    pos_images = tensor_to_image((render_out.pos + 0.5).clamp(0, 1), batched=True)
+    normal_images = tensor_to_image(
+        (render_out.normal / 2 + 0.5).clamp(0, 1), batched=True
+    )
+    
+    control_images = (
+        torch.cat(
+            [
+                (render_out.pos + 0.5).clamp(0, 1),
+                (render_out.normal / 2 + 0.5).clamp(0, 1),
+            ],
+            dim=-1,
+        )
+        .permute(0, 3, 1, 2)
+        .to(device)
+    )
+
+    # Prepare image
+    reference_image = Image.open(image) if isinstance(image, str) else image
+    if reference_image.mode == "RGBA":
+        reference_image = preprocess_image(reference_image, height, width)
+
+    pipe_kwargs = {}
+    if seed != -1 and isinstance(seed, int):
+        pipe_kwargs["generator"] = torch.Generator(device=device).manual_seed(seed)
+
+    images = pipe(
+        text,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=num_views,
+        control_image=control_images,
+        control_conditioning_scale=1.0,
+        reference_image=reference_image,
+        reference_conditioning_scale=reference_conditioning_scale,
+        negative_prompt=negative_prompt,
+        cross_attention_kwargs={"scale": lora_scale},
+        **pipe_kwargs,
+    ).images
+
+    return images, pos_images, normal_images, reference_image
+
+
+def create_image_grid(images: List[Image.Image], rows: int = 1) -> Image.Image:
+    return make_image_grid(images, rows=rows)
+
+
+def pil_to_torch_tensor(image: Image.Image) -> torch.Tensor:
+    import numpy as np
+    array = np.array(image)
+    tensor = torch.from_numpy(array).float() / 255.0
+    
+    # Add batch dimension if needed
+    if tensor.dim() == 3:
+        tensor = tensor.unsqueeze(0)
+    
+    return tensor
+
+
+def pils_to_torch_tensor(images: List[Image.Image]) -> torch.Tensor:
+    tensors = [pil_to_torch_tensor(img).squeeze(0) for img in images]
+    return torch.stack(tensors, dim=0)
+
+
+def prepare_texture_pipeline(
+    upscaler_ckpt_path: Optional[str] = None,
+    inpaint_ckpt_path: Optional[str] = None,
+    device: str = "cuda",
+    use_mmgp: bool = False,
+):
+    """
+    Prepare texture projection pipeline
+    
+    Args:
+        upscaler_ckpt_path: Path to upscaler checkpoint (optional)
+        inpaint_ckpt_path: Path to inpaint checkpoint (optional)
+        device: Device for computation
+        use_mmgp: Use mmgp for memory optimization
+    """
+    from .mvadapter.pipelines.pipeline_texture import TexturePipeline
+    
+    if upscaler_ckpt_path and not os.path.exists(upscaler_ckpt_path):
+        print(f"Warning: Upscaler checkpoint not found: {upscaler_ckpt_path}, using None")
+        upscaler_ckpt_path = None
+        
+    if inpaint_ckpt_path and not os.path.exists(inpaint_ckpt_path):
+        print(f"Warning: Inpaint checkpoint not found: {inpaint_ckpt_path}, using None")
+        inpaint_ckpt_path = None
+    
+    texture_pipe = TexturePipeline(
+        upscaler_ckpt_path=upscaler_ckpt_path,
+        inpaint_ckpt_path=inpaint_ckpt_path,
+        device=device
+    )
+    
+    # Apply mmgp optimization if available and requested
+    if use_mmgp and MMGP_AVAILABLE:
+        import gc
+        torch.cuda.empty_cache()
+        gc.collect()
+        
+        all_models = {}
+        
+        if hasattr(texture_pipe, 'upscaler') and texture_pipe.upscaler is not None:
+            all_models["upscaler"] = texture_pipe.upscaler
+            
+        if hasattr(texture_pipe, 'inpainter') and texture_pipe.inpainter is not None:
+            all_models["inpainter"] = texture_pipe.inpainter
+        
+        if all_models:
+            offload.profile(all_models, profile_type.HighRAM_LowVRAM)
+            print("mmgp profiling applied to texture pipeline successfully.")
+        else:
+            print("No models loaded for texture pipeline, skipping mmgp profiling.")
+    
+    return texture_pipe
+
+
+def run_texture_pipeline(
+    texture_pipe,
+    multiview_images: List[Image.Image],
+    mesh_path: str,
+    save_dir: str,
+    save_name: str = "textured_model",
+    uv_size: int = 4096,
+    view_upscale: bool = True,
+    inpaint_mode: str = "view",
+    uv_unwarp: bool = True,
+    preprocess_mesh: bool = False,
+    move_to_center: bool = False,
+    front_x: bool = True,
+    camera_azimuth_deg: List[float] = None,
+    camera_elevation_deg: List[float] = None,
+    camera_distance: float = 1.0,
+    camera_ortho_scale: float = 1.1,
+    debug_mode: bool = False,
+    apply_dilate: bool = True,
+):
+    """
+    Execute texture projection pipeline
+    
+    Args:
+        texture_pipe: Prepared texture pipeline
+        multiview_images: List of PIL images (6 views)
+        mesh_path: Path to 3D mesh file
+        save_dir: Directory to save results
+        save_name: Name for saved files
+        uv_size: UV texture resolution
+        view_upscale: Enable view upscaling
+        inpaint_mode: Inpainting mode ("none", "uv", "view")
+        uv_unwarp: Enable UV unwrapping
+        preprocess_mesh: Enable mesh preprocessing
+        move_to_center: Move mesh to center
+        front_x: Front face along X axis
+        camera_azimuth_deg: Camera azimuth angles
+        camera_elevation_deg: Camera elevation angles
+        camera_distance: Camera distance
+        camera_ortho_scale: Orthographic scale
+        debug_mode: Enable debug mode
+        apply_dilate: Apply dilate to remove texture seams
+    """
+    from .mvadapter.pipelines.pipeline_texture import ModProcessConfig
+    from .mvadapter.utils import make_image_grid
+    
+    if not mesh_path or not os.path.exists(mesh_path):
+        raise ValueError(f"Mesh file does not exist: {mesh_path}")
+    
+    os.makedirs(save_dir, exist_ok=True)
+    
+    if camera_azimuth_deg is None:
+        camera_azimuth_deg = [0, 90, 180, 270, 180, 180]
+    if camera_elevation_deg is None:
+        camera_elevation_deg = [0, 0, 0, 0, 89.99, -89.99]
+    
+    azimuth_deg_corrected = [x - 90 for x in camera_azimuth_deg]
+    
+    grid_image = make_image_grid(multiview_images, rows=1)
+    temp_grid_path = os.path.join(save_dir, f"{save_name}_temp_grid.png")
+    grid_image.save(temp_grid_path)
+    
+    try:
+        rgb_process_config = ModProcessConfig(
+            view_upscale=view_upscale,
+            inpaint_mode=inpaint_mode
+        )
+        
+        output = texture_pipe(
+            mesh_path=mesh_path,
+            save_dir=save_dir,
+            save_name=save_name,
+            move_to_center=move_to_center,
+            front_x=front_x,
+            uv_unwarp=uv_unwarp,
+            preprocess_mesh=preprocess_mesh,
+            uv_size=uv_size,
+            rgb_path=temp_grid_path,
+            rgb_process_config=rgb_process_config,
+            camera_elevation_deg=camera_elevation_deg,
+            camera_azimuth_deg=azimuth_deg_corrected,
+            camera_distance=camera_distance,
+            camera_ortho_scale=camera_ortho_scale,
+            debug_mode=debug_mode,
+            apply_dilate=apply_dilate,
+        )
+        
+        if os.path.exists(temp_grid_path):
+            os.remove(temp_grid_path)
+        
+        return output
+        
+    except Exception as e:
+        if os.path.exists(temp_grid_path):
+            os.remove(temp_grid_path)
+        raise e
+
+
+def download_texture_checkpoints(texture_ckpt_dir, upscaler_ckpt_path, inpaint_ckpt_path):
+    """Download texture checkpoint files if they don't exist"""
+    import subprocess
+    
+    os.makedirs(texture_ckpt_dir, exist_ok=True)
+    
+    checkpoints = [
+        {
+            "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+            "path": upscaler_ckpt_path,
+            "name": "RealESRGAN upscaler"
+        },
+        {
+            "url": "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt", 
+            "path": inpaint_ckpt_path,
+            "name": "Big-LaMa inpainter"
+        }
+    ]
+    
+    for ckpt in checkpoints:
+        if not os.path.exists(ckpt["path"]):
+            print(f"Downloading {ckpt['name']} to {ckpt['path']}...")
+            
+            success = False
+            
+            # Try wget first
+            try:
+                subprocess.run([
+                    "wget", ckpt["url"], "-O", ckpt["path"]
+                ], check=True, capture_output=True, text=True)
+                print(f"Successfully downloaded {ckpt['name']} using wget")
+                success = True
+            except (subprocess.CalledProcessError, FileNotFoundError):
+                pass
+            
+            if not success:
+                try:
+                    subprocess.run([
+                        "curl", "-L", ckpt["url"], "-o", ckpt["path"]
+                    ], check=True, capture_output=True, text=True)
+                    print(f"Successfully downloaded {ckpt['name']} using curl")
+                    success = True
+                except (subprocess.CalledProcessError, FileNotFoundError):
+                    pass
+            
+            if not success:
+                try:
+                    import urllib.request
+                    urllib.request.urlretrieve(ckpt["url"], ckpt["path"])
+                    print(f"Successfully downloaded {ckpt['name']} using urllib")
+                    success = True
+                except Exception as e:
+                    print(f"Failed to download {ckpt['name']} using urllib: {e}")
+            
+            if not success:
+                print(f"Failed to download {ckpt['name']}. Please download manually:")
+                print(f"wget {ckpt['url']} -O {ckpt['path']}")
+                print(f"or")
+                print(f"curl -L {ckpt['url']} -o {ckpt['path']}")
+        else:
+            print(f"{ckpt['name']} already exists at {ckpt['path']}") 
+
+
+def prepare_tg2mv_pipeline(
+    base_model: str,
+    vae_model: Optional[str] = None,
+    lora_model: Optional[str] = None,
+    adapter_path: str = "huanngzh/mv-adapter",
+    scheduler: str = "ddpm",
+    num_views: int = 6,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.float16,
+    use_mmgp: bool = False,
+    adapter_local_path: Optional[str] = None,
+):
+    """
+    Prepare MV-Adapter TG2MV (Text-Guided to Multi-View) pipeline
+    
+    Args:
+        base_model: Base Stable Diffusion XL model
+        vae_model: Custom VAE model (optional)
+        lora_model: LoRA model (optional)
+        adapter_path: Path to adapter weights
+        scheduler: Scheduler type ("ddpm")
+        num_views: Number of views to generate
+        device: Device for computation
+        dtype: Data type
+        use_mmgp: Use mmgp for memory optimization
+        adapter_local_path: Local path for adapter download
+    """
+    import gc
+    torch.cuda.empty_cache()
+    gc.collect()
+    
+    pipe_kwargs = {}
+    if vae_model is not None:
+        pipe_kwargs["vae"] = AutoencoderKL.from_pretrained(vae_model, torch_dtype=dtype)
+
+    pipe = MVAdapterT2MVSDXLPipeline.from_pretrained(base_model, torch_dtype=dtype, **pipe_kwargs)
+
+    pipe.scheduler = ShiftSNRScheduler.from_scheduler(
+        pipe.scheduler,
+        shift_mode="interpolated",
+        shift_scale=8.0,
+        scheduler_class=DDPMScheduler,
+    )
+    
+    pipe.init_custom_adapter(
+        num_views=num_views, self_attn_processor=DecoupledMVRowColSelfAttnProcessor2_0
+    )
+    
+    weight_name = "mvadapter_tg2mv_sdxl.safetensors"
+    pipe.load_custom_adapter(adapter_path, weight_name=weight_name, local_cache_dir=adapter_local_path)
+
+    pipe.to(device=device, dtype=dtype)
+    pipe.cond_encoder.to(device=device, dtype=dtype)
+
+    if lora_model is not None:
+        model_, name_ = lora_model.rsplit("/", 1)
+        pipe.load_lora_weights(model_, weight_name=name_)
+
+    pipe.enable_vae_slicing()
+    pipe.enable_vae_tiling()  
+    
+    if hasattr(pipe, 'enable_model_cpu_offload'):
+        if not use_mmgp:  
+            pipe.enable_model_cpu_offload()
+            print("[INFO] Включен CPU offload для экономии VRAM")
+
+    if use_mmgp and MMGP_AVAILABLE:
+        torch.cuda.empty_cache()
+        gc.collect()
+        
+        all_models = {
+            "vae": pipe.vae,
+            "text_encoder": pipe.text_encoder,
+            "text_encoder_2": pipe.text_encoder_2,
+            "unet": pipe.unet,
+            "cond_encoder": pipe.cond_encoder,  
+        }
+        
+        if hasattr(pipe, 'image_encoder') and pipe.image_encoder is not None:
+            all_models["image_encoder"] = pipe.image_encoder
+        
+        try:
+            offload.profile(all_models, profile_type.HighRAM_LowVRAM)
+            print("mmgp profiling applied successfully.")
+        except Exception as e:
+            print(f"[WARNING] mmgp failed, continuing without it: {e}")
+    
+    torch.cuda.empty_cache()
+    gc.collect()
+
+    return pipe
+
+
+def run_tg2mv_pipeline(
+    pipe,
+    mesh_path: str,
+    num_views: int,
+    text: str,
+    height: int,
+    width: int,
+    num_inference_steps: int,
+    guidance_scale: float,
+    seed: int,
+    negative_prompt: str = "watermark, ugly, deformed, noisy, blurry, low contrast",
+    lora_scale: float = 1.0,
+    device: str = "cuda",
+):
+    """
+    Execute text-guided multiview generation (TG2MV)
+    
+    Args:
+        pipe: TG2MV pipeline
+        mesh_path: Path to 3D mesh file for guidance
+        num_views: Number of views to generate
+        text: Text prompt
+        height: Image height
+        width: Image width
+        num_inference_steps: Number of denoising steps
+        guidance_scale: Guidance scale
+        seed: Random seed
+        negative_prompt: Negative prompt
+        lora_scale: LoRA scale
+        device: Device for computation
+    """
+    cameras = get_orthogonal_camera(
+        elevation_deg=[0, 0, 0, 0, 89.99, -89.99],
+        distance=[1.8] * num_views,
+        left=-0.55,
+        right=0.55,
+        bottom=-0.55,
+        top=0.55,
+        azimuth_deg=[x - 90 for x in [0, 90, 180, 270, 180, 180]],
+        device=device,
+    )
+    ctx = NVDiffRastContextWrapper(device=device)
+
+    mesh = load_mesh(mesh_path, rescale=True, device=device)
+    render_out = render(
+        ctx,
+        mesh,
+        cameras,
+        height=height,
+        width=width,
+        render_attr=False,
+        normal_background=0.0,
+    )
+    
+    pos_images = tensor_to_image((render_out.pos + 0.5).clamp(0, 1), batched=True)
+    normal_images = tensor_to_image(
+        (render_out.normal / 2 + 0.5).clamp(0, 1), batched=True
+    )
+    
+    control_images = (
+        torch.cat(
+            [
+                (render_out.pos + 0.5).clamp(0, 1),
+                (render_out.normal / 2 + 0.5).clamp(0, 1),
+            ],
+            dim=-1,
+        )
+        .permute(0, 3, 1, 2)
+        .to(device)
+    )
+
+    pipe_kwargs = {}
+    if seed != -1 and isinstance(seed, int):
+        pipe_kwargs["generator"] = torch.Generator(device=device).manual_seed(seed)
+
+    images = pipe(
+        text,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=num_views,
+        control_image=control_images,
+        control_conditioning_scale=1.0,
+        negative_prompt=negative_prompt,
+        cross_attention_kwargs={"scale": lora_scale},
+        **pipe_kwargs,
+    ).images
+
+    return images, pos_images, normal_images 
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/__init__.py b/Gen_3D_Modules/Stable3DGen/__init__.py
new file mode 100644
index 0000000..22a7919
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/__init__.py
@@ -0,0 +1,65 @@
+#__init__.py
+import os
+import sys
+import torch
+import logging
+import platform
+import folder_paths
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger('ComfyUI-Hi3DGen')
+
+# Add parent directory to Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(current_dir)
+
+# Add both current and parent dir to handle different installation scenarios
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+if parent_dir not in sys.path:
+    sys.path.insert(0, parent_dir)
+
+# Add trellis package path
+trellis_path = os.path.join(current_dir, "trellis")
+if os.path.exists(trellis_path) and trellis_path not in sys.path:
+    sys.path.insert(0, trellis_path)
+    # logger.info(f"Added trellis path to sys.path: {trellis_path}")
+
+# Add stablx package path
+stablex_path = os.path.join(current_dir, "stablex")
+if os.path.exists(trellis_path) and trellis_path not in sys.path:
+    sys.path.insert(0, trellis_path)
+    # logger.info(f"Added stablex path to sys.path: {trellis_path}")
+
+# Verify trellis package is importable
+try:
+    import trellis
+    # logger.info("Trellis package imported successfully")
+except ImportError as e:
+    logger.error(f"Failed to import trellis package: {e}")
+    logger.error(f"Current sys.path: {sys.path}")
+    raise
+
+# Verify stablex package is importable
+try:
+    import stablex
+    # logger.info("stablex package imported successfully")
+except ImportError as e:
+    logger.error(f"Failed to import stablex package: {e}")
+    logger.error(f"Current sys.path: {sys.path}")
+    raise
+
+# Register model paths with ComfyUI
+try:
+    folder_paths.add_model_folder_path("trellis", os.path.join(folder_paths.models_dir, "trellis"))
+    folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.models_dir, "checkpoints"))
+except Exception as e:
+    logger.error(f"Error registering model paths: {e}")
+
+# Register model paths with ComfyUI
+try:
+    folder_paths.add_model_folder_path("stablex", os.path.join(folder_paths.models_dir, "stablex"))
+except Exception as e:
+    logger.error(f"Error registering model paths: {e}")
+
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/__init__.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/__init__.py
new file mode 100644
index 0000000..ae847e4
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+__version__ = "0.0.1"
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/__init__.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/__init__.py
new file mode 100644
index 0000000..b88da6b
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/backbones.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/backbones.py
new file mode 100644
index 0000000..53fe837
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/backbones.py
@@ -0,0 +1,156 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
+
+
+class Weights(Enum):
+    LVD142M = "LVD142M"
+
+
+def _make_dinov2_model(
+    *,
+    arch_name: str = "vit_large",
+    img_size: int = 518,
+    patch_size: int = 14,
+    init_values: float = 1.0,
+    ffn_layer: str = "mlp",
+    block_chunks: int = 0,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.LVD142M,
+    **kwargs,
+):
+    from ..models import vision_transformer as vits
+
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+    vit_kwargs = dict(
+        img_size=img_size,
+        patch_size=patch_size,
+        init_values=init_values,
+        ffn_layer=ffn_layer,
+        block_chunks=block_chunks,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+    )
+    vit_kwargs.update(**kwargs)
+    model = vits.__dict__[arch_name](**vit_kwargs)
+
+    if pretrained:
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_pretrain.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        model.load_state_dict(state_dict, strict=True)
+
+    return model
+
+
+def dinov2_vits14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitb14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitl14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitg14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_small",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_base",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_large",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/classifiers.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/classifiers.py
new file mode 100644
index 0000000..3f0841e
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/classifiers.py
@@ -0,0 +1,268 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+import torch.nn as nn
+
+from .backbones import _make_dinov2_model
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name
+
+
+class Weights(Enum):
+    IMAGENET1K = "IMAGENET1K"
+
+
+def _make_dinov2_linear_classification_head(
+    *,
+    arch_name: str = "vit_large",
+    patch_size: int = 14,
+    embed_dim: int = 1024,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    num_register_tokens: int = 0,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    linear_head = nn.Linear((1 + layers) * embed_dim, 1_000)
+
+    if pretrained:
+        model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        layers_str = str(layers) if layers == 4 else ""
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_linear{layers_str}_head.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        linear_head.load_state_dict(state_dict, strict=True)
+
+    return linear_head
+
+
+class _LinearClassifierWrapper(nn.Module):
+    def __init__(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4):
+        super().__init__()
+        self.backbone = backbone
+        self.linear_head = linear_head
+        self.layers = layers
+
+    def forward(self, x):
+        if self.layers == 1:
+            x = self.backbone.forward_features(x)
+            cls_token = x["x_norm_clstoken"]
+            patch_tokens = x["x_norm_patchtokens"]
+            # fmt: off
+            linear_input = torch.cat([
+                cls_token,
+                patch_tokens.mean(dim=1),
+            ], dim=1)
+            # fmt: on
+        elif self.layers == 4:
+            x = self.backbone.get_intermediate_layers(x, n=4, return_class_token=True)
+            # fmt: off
+            linear_input = torch.cat([
+                x[0][1],
+                x[1][1],
+                x[2][1],
+                x[3][1],
+                x[3][0].mean(dim=1),
+            ], dim=1)
+            # fmt: on
+        else:
+            assert False, f"Unsupported number of layers: {self.layers}"
+        return self.linear_head(linear_input)
+
+
+def _make_dinov2_linear_classifier(
+    *,
+    arch_name: str = "vit_large",
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    **kwargs,
+):
+    backbone = _make_dinov2_model(
+        arch_name=arch_name,
+        pretrained=pretrained,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+        **kwargs,
+    )
+
+    embed_dim = backbone.embed_dim
+    patch_size = backbone.patch_size
+    linear_head = _make_dinov2_linear_classification_head(
+        arch_name=arch_name,
+        patch_size=patch_size,
+        embed_dim=embed_dim,
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=num_register_tokens,
+    )
+
+    return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers)
+
+
+def dinov2_vits14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_small",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_base",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_large",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_lc(
+    *,
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.IMAGENET1K,
+    **kwargs,
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_giant2",
+        layers=layers,
+        ffn_layer="swiglufused",
+        pretrained=pretrained,
+        weights=weights,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_small",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_base",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_large",
+        layers=layers,
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg_lc(
+    *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs
+):
+    """
+    Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k.
+    """
+    return _make_dinov2_linear_classifier(
+        arch_name="vit_giant2",
+        layers=layers,
+        ffn_layer="swiglufused",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/__init__.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/__init__.py
new file mode 100644
index 0000000..91716e5
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .decode_heads import BNHead, DPTHead
+from .encoder_decoder import DepthEncoderDecoder
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/decode_heads.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/decode_heads.py
new file mode 100644
index 0000000..f455acc
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/decode_heads.py
@@ -0,0 +1,747 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import copy
+from functools import partial
+import math
+import warnings
+
+import torch
+import torch.nn as nn
+
+from .ops import resize
+
+
+# XXX: (Untested) replacement for mmcv.imdenormalize()
+def _imdenormalize(img, mean, std, to_bgr=True):
+    import numpy as np
+
+    mean = mean.reshape(1, -1).astype(np.float64)
+    std = std.reshape(1, -1).astype(np.float64)
+    img = (img * std) + mean
+    if to_bgr:
+        img = img[::-1]
+    return img
+
+
+class DepthBaseDecodeHead(nn.Module):
+    """Base class for BaseDecodeHead.
+
+    Args:
+        in_channels (List): Input channels.
+        channels (int): Channels after modules, before conv_depth.
+        conv_layer (nn.Module): Conv layers. Default: None.
+        act_layer (nn.Module): Activation layers. Default: nn.ReLU.
+        loss_decode (dict): Config of decode loss.
+            Default: ().
+        sampler (dict|None): The config of depth map sampler.
+            Default: None.
+        align_corners (bool): align_corners argument of F.interpolate.
+            Default: False.
+        min_depth (int): Min depth in dataset setting.
+            Default: 1e-3.
+        max_depth (int): Max depth in dataset setting.
+            Default: None.
+        norm_layer (dict|None): Norm layers.
+            Default: None.
+        classify (bool): Whether predict depth in a cls.-reg. manner.
+            Default: False.
+        n_bins (int): The number of bins used in cls. step.
+            Default: 256.
+        bins_strategy (str): The discrete strategy used in cls. step.
+            Default: 'UD'.
+        norm_strategy (str): The norm strategy on cls. probability
+            distribution. Default: 'linear'
+        scale_up (str): Whether predict depth in a scale-up manner.
+            Default: False.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        conv_layer=None,
+        act_layer=nn.ReLU,
+        channels=96,
+        loss_decode=(),
+        sampler=None,
+        align_corners=False,
+        min_depth=1e-3,
+        max_depth=None,
+        norm_layer=None,
+        classify=False,
+        n_bins=256,
+        bins_strategy="UD",
+        norm_strategy="linear",
+        scale_up=False,
+    ):
+        super(DepthBaseDecodeHead, self).__init__()
+
+        self.in_channels = in_channels
+        self.channels = channels
+        self.conf_layer = conv_layer
+        self.act_layer = act_layer
+        self.loss_decode = loss_decode
+        self.align_corners = align_corners
+        self.min_depth = min_depth
+        self.max_depth = max_depth
+        self.norm_layer = norm_layer
+        self.classify = classify
+        self.n_bins = n_bins
+        self.scale_up = scale_up
+
+        if self.classify:
+            assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID"
+            assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid"
+
+            self.bins_strategy = bins_strategy
+            self.norm_strategy = norm_strategy
+            self.softmax = nn.Softmax(dim=1)
+            self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1)
+
+        self.relu = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, inputs, img_metas):
+        """Placeholder of forward function."""
+        pass
+
+    def forward_train(self, img, inputs, img_metas, depth_gt):
+        """Forward function for training.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): GT depth
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        depth_pred = self.forward(inputs, img_metas)
+        losses = self.losses(depth_pred, depth_gt)
+
+        log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0])
+        losses.update(**log_imgs)
+
+        return losses
+
+    def forward_test(self, inputs, img_metas):
+        """Forward function for testing.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+
+        Returns:
+            Tensor: Output depth map.
+        """
+        return self.forward(inputs, img_metas)
+
+    def depth_pred(self, feat):
+        """Prediction each pixel."""
+        if self.classify:
+            logit = self.conv_depth(feat)
+
+            if self.bins_strategy == "UD":
+                bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+            elif self.bins_strategy == "SID":
+                bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device)
+
+            # following Adabins, default linear
+            if self.norm_strategy == "linear":
+                logit = torch.relu(logit)
+                eps = 0.1
+                logit = logit + eps
+                logit = logit / logit.sum(dim=1, keepdim=True)
+            elif self.norm_strategy == "softmax":
+                logit = torch.softmax(logit, dim=1)
+            elif self.norm_strategy == "sigmoid":
+                logit = torch.sigmoid(logit)
+                logit = logit / logit.sum(dim=1, keepdim=True)
+
+            output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1)
+
+        else:
+            if self.scale_up:
+                output = self.sigmoid(self.conv_depth(feat)) * self.max_depth
+            else:
+                output = self.relu(self.conv_depth(feat)) + self.min_depth
+        return output
+
+    def losses(self, depth_pred, depth_gt):
+        """Compute depth loss."""
+        loss = dict()
+        depth_pred = resize(
+            input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False
+        )
+        if not isinstance(self.loss_decode, nn.ModuleList):
+            losses_decode = [self.loss_decode]
+        else:
+            losses_decode = self.loss_decode
+        for loss_decode in losses_decode:
+            if loss_decode.loss_name not in loss:
+                loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt)
+            else:
+                loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt)
+        return loss
+
+    def log_images(self, img_path, depth_pred, depth_gt, img_meta):
+        import numpy as np
+
+        show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0))
+        show_img = show_img.numpy().astype(np.float32)
+        show_img = _imdenormalize(
+            show_img,
+            img_meta["img_norm_cfg"]["mean"],
+            img_meta["img_norm_cfg"]["std"],
+            img_meta["img_norm_cfg"]["to_rgb"],
+        )
+        show_img = np.clip(show_img, 0, 255)
+        show_img = show_img.astype(np.uint8)
+        show_img = show_img[:, :, ::-1]
+        show_img = show_img.transpose(0, 2, 1)
+        show_img = show_img.transpose(1, 0, 2)
+
+        depth_pred = depth_pred / torch.max(depth_pred)
+        depth_gt = depth_gt / torch.max(depth_gt)
+
+        depth_pred_color = copy.deepcopy(depth_pred.detach().cpu())
+        depth_gt_color = copy.deepcopy(depth_gt.detach().cpu())
+
+        return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color}
+
+
+class BNHead(DepthBaseDecodeHead):
+    """Just a batchnorm."""
+
+    def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs):
+        super().__init__(**kwargs)
+        self.input_transform = input_transform
+        self.in_index = in_index
+        self.upsample = upsample
+        # self.bn = nn.SyncBatchNorm(self.in_channels)
+        if self.classify:
+            self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1)
+        else:
+            self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1)
+
+    def _transform_inputs(self, inputs):
+        """Transform inputs for decoder.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            Tensor: The transformed inputs
+        """
+
+        if "concat" in self.input_transform:
+            inputs = [inputs[i] for i in self.in_index]
+            if "resize" in self.input_transform:
+                inputs = [
+                    resize(
+                        input=x,
+                        size=[s * self.upsample for s in inputs[0].shape[2:]],
+                        mode="bilinear",
+                        align_corners=self.align_corners,
+                    )
+                    for x in inputs
+                ]
+            inputs = torch.cat(inputs, dim=1)
+        elif self.input_transform == "multiple_select":
+            inputs = [inputs[i] for i in self.in_index]
+        else:
+            inputs = inputs[self.in_index]
+
+        return inputs
+
+    def _forward_feature(self, inputs, img_metas=None, **kwargs):
+        """Forward function for feature maps before classifying each pixel with
+        ``self.cls_seg`` fc.
+        Args:
+            inputs (list[Tensor]): List of multi-level img features.
+        Returns:
+            feats (Tensor): A tensor of shape (batch_size, self.channels,
+                H, W) which is feature map for last layer of decoder head.
+        """
+        # accept lists (for cls token)
+        inputs = list(inputs)
+        for i, x in enumerate(inputs):
+            if len(x) == 2:
+                x, cls_token = x[0], x[1]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                cls_token = cls_token[:, :, None, None].expand_as(x)
+                inputs[i] = torch.cat((x, cls_token), 1)
+            else:
+                x = x[0]
+                if len(x.shape) == 2:
+                    x = x[:, :, None, None]
+                inputs[i] = x
+        x = self._transform_inputs(inputs)
+        # feats = self.bn(x)
+        return x
+
+    def forward(self, inputs, img_metas=None, **kwargs):
+        """Forward function."""
+        output = self._forward_feature(inputs, img_metas=img_metas, **kwargs)
+        output = self.depth_pred(output)
+        return output
+
+
+class ConvModule(nn.Module):
+    """A conv block that bundles conv/norm/activation layers.
+
+    This block simplifies the usage of convolution layers, which are commonly
+    used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
+    It is based upon three build methods: `build_conv_layer()`,
+    `build_norm_layer()` and `build_activation_layer()`.
+
+    Besides, we add some additional features in this module.
+    1. Automatically set `bias` of the conv layer.
+    2. Spectral norm is supported.
+    3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
+    supports zero and circular padding, and we add "reflect" padding mode.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+            Same as that in ``nn._ConvNd``.
+        out_channels (int): Number of channels produced by the convolution.
+            Same as that in ``nn._ConvNd``.
+        kernel_size (int | tuple[int]): Size of the convolving kernel.
+            Same as that in ``nn._ConvNd``.
+        stride (int | tuple[int]): Stride of the convolution.
+            Same as that in ``nn._ConvNd``.
+        padding (int | tuple[int]): Zero-padding added to both sides of
+            the input. Same as that in ``nn._ConvNd``.
+        dilation (int | tuple[int]): Spacing between kernel elements.
+            Same as that in ``nn._ConvNd``.
+        groups (int): Number of blocked connections from input channels to
+            output channels. Same as that in ``nn._ConvNd``.
+        bias (bool | str): If specified as `auto`, it will be decided by the
+            norm_layer. Bias will be set as True if `norm_layer` is None, otherwise
+            False. Default: "auto".
+        conv_layer (nn.Module): Convolution layer. Default: None,
+            which means using conv2d.
+        norm_layer (nn.Module): Normalization layer. Default: None.
+        act_layer (nn.Module): Activation layer. Default: nn.ReLU.
+        inplace (bool): Whether to use inplace mode for activation.
+            Default: True.
+        with_spectral_norm (bool): Whether use spectral norm in conv module.
+            Default: False.
+        padding_mode (str): If the `padding_mode` has not been supported by
+            current `Conv2d` in PyTorch, we will use our own padding layer
+            instead. Currently, we support ['zeros', 'circular'] with official
+            implementation and ['reflect'] with our own implementation.
+            Default: 'zeros'.
+        order (tuple[str]): The order of conv/norm/activation layers. It is a
+            sequence of "conv", "norm" and "act". Common examples are
+            ("conv", "norm", "act") and ("act", "conv", "norm").
+            Default: ('conv', 'norm', 'act').
+    """
+
+    _abbr_ = "conv_block"
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias="auto",
+        conv_layer=nn.Conv2d,
+        norm_layer=None,
+        act_layer=nn.ReLU,
+        inplace=True,
+        with_spectral_norm=False,
+        padding_mode="zeros",
+        order=("conv", "norm", "act"),
+    ):
+        super(ConvModule, self).__init__()
+        official_padding_mode = ["zeros", "circular"]
+        self.conv_layer = conv_layer
+        self.norm_layer = norm_layer
+        self.act_layer = act_layer
+        self.inplace = inplace
+        self.with_spectral_norm = with_spectral_norm
+        self.with_explicit_padding = padding_mode not in official_padding_mode
+        self.order = order
+        assert isinstance(self.order, tuple) and len(self.order) == 3
+        assert set(order) == set(["conv", "norm", "act"])
+
+        self.with_norm = norm_layer is not None
+        self.with_activation = act_layer is not None
+        # if the conv layer is before a norm layer, bias is unnecessary.
+        if bias == "auto":
+            bias = not self.with_norm
+        self.with_bias = bias
+
+        if self.with_explicit_padding:
+            if padding_mode == "zeros":
+                padding_layer = nn.ZeroPad2d
+            else:
+                raise AssertionError(f"Unsupported padding mode: {padding_mode}")
+            self.pad = padding_layer(padding)
+
+        # reset padding to 0 for conv module
+        conv_padding = 0 if self.with_explicit_padding else padding
+        # build convolution layer
+        self.conv = self.conv_layer(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=conv_padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+        # export the attributes of self.conv to a higher level for convenience
+        self.in_channels = self.conv.in_channels
+        self.out_channels = self.conv.out_channels
+        self.kernel_size = self.conv.kernel_size
+        self.stride = self.conv.stride
+        self.padding = padding
+        self.dilation = self.conv.dilation
+        self.transposed = self.conv.transposed
+        self.output_padding = self.conv.output_padding
+        self.groups = self.conv.groups
+
+        if self.with_spectral_norm:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+        # build normalization layers
+        if self.with_norm:
+            # norm layer is after conv layer
+            if order.index("norm") > order.index("conv"):
+                norm_channels = out_channels
+            else:
+                norm_channels = in_channels
+            norm = partial(norm_layer, num_features=norm_channels)
+            self.add_module("norm", norm)
+            if self.with_bias:
+                from torch.nnModules.batchnorm import _BatchNorm
+                from torch.nnModules.instancenorm import _InstanceNorm
+
+                if isinstance(norm, (_BatchNorm, _InstanceNorm)):
+                    warnings.warn("Unnecessary conv bias before batch/instance norm")
+        else:
+            self.norm_name = None
+
+        # build activation layer
+        if self.with_activation:
+            # nn.Tanh has no 'inplace' argument
+            # (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.HSigmoid, nn.Swish, nn.GELU)
+            if not isinstance(act_layer, (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.GELU)):
+                act_layer = partial(act_layer, inplace=inplace)
+            self.activate = act_layer()
+
+        # Use msra init by default
+        self.init_weights()
+
+    @property
+    def norm(self):
+        if self.norm_name:
+            return getattr(self, self.norm_name)
+        else:
+            return None
+
+    def init_weights(self):
+        # 1. It is mainly for customized conv layers with their own
+        #    initialization manners by calling their own ``init_weights()``,
+        #    and we do not want ConvModule to override the initialization.
+        # 2. For customized conv layers without their own initialization
+        #    manners (that is, they don't have their own ``init_weights()``)
+        #    and PyTorch's conv layers, they will be initialized by
+        #    this method with default ``kaiming_init``.
+        # Note: For PyTorch's conv layers, they will be overwritten by our
+        #    initialization implementation using default ``kaiming_init``.
+        if not hasattr(self.conv, "init_weights"):
+            if self.with_activation and isinstance(self.act_layer, nn.LeakyReLU):
+                nonlinearity = "leaky_relu"
+                a = 0.01  # XXX: default negative_slope
+            else:
+                nonlinearity = "relu"
+                a = 0
+            if hasattr(self.conv, "weight") and self.conv.weight is not None:
+                nn.init.kaiming_normal_(self.conv.weight, a=a, mode="fan_out", nonlinearity=nonlinearity)
+            if hasattr(self.conv, "bias") and self.conv.bias is not None:
+                nn.init.constant_(self.conv.bias, 0)
+        if self.with_norm:
+            if hasattr(self.norm, "weight") and self.norm.weight is not None:
+                nn.init.constant_(self.norm.weight, 1)
+            if hasattr(self.norm, "bias") and self.norm.bias is not None:
+                nn.init.constant_(self.norm.bias, 0)
+
+    def forward(self, x, activate=True, norm=True):
+        for layer in self.order:
+            if layer == "conv":
+                if self.with_explicit_padding:
+                    x = self.pad(x)
+                x = self.conv(x)
+            elif layer == "norm" and norm and self.with_norm:
+                x = self.norm(x)
+            elif layer == "act" and activate and self.with_activation:
+                x = self.activate(x)
+        return x
+
+
+class Interpolate(nn.Module):
+    def __init__(self, scale_factor, mode, align_corners=False):
+        super(Interpolate, self).__init__()
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+        return x
+
+
+class HeadDepth(nn.Module):
+    def __init__(self, features):
+        super(HeadDepth, self).__init__()
+        self.head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+        )
+
+    def forward(self, x):
+        x = self.head(x)
+        return x
+
+
+class ReassembleBlocks(nn.Module):
+    """ViTPostProcessBlock, process cls_token in ViT backbone output and
+    rearrange the feature vector to feature map.
+    Args:
+        in_channels (int): ViT feature channels. Default: 768.
+        out_channels (List): output channels of each stage.
+            Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+    """
+
+    def __init__(self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16):
+        super(ReassembleBlocks, self).__init__()
+
+        assert readout_type in ["ignore", "add", "project"]
+        self.readout_type = readout_type
+        self.patch_size = patch_size
+
+        self.projects = nn.ModuleList(
+            [
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=out_channel,
+                    kernel_size=1,
+                    act_layer=None,
+                )
+                for out_channel in out_channels
+            ]
+        )
+
+        self.resize_layers = nn.ModuleList(
+            [
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
+                ),
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
+                ),
+                nn.Identity(),
+                nn.Conv2d(
+                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
+                ),
+            ]
+        )
+        if self.readout_type == "project":
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
+
+    def forward(self, inputs):
+        assert isinstance(inputs, list)
+        out = []
+        for i, x in enumerate(inputs):
+            assert len(x) == 2
+            x, cls_token = x[0], x[1]
+            feature_shape = x.shape
+            if self.readout_type == "project":
+                x = x.flatten(2).permute((0, 2, 1))
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+                x = x.permute(0, 2, 1).reshape(feature_shape)
+            elif self.readout_type == "add":
+                x = x.flatten(2) + cls_token.unsqueeze(-1)
+                x = x.reshape(feature_shape)
+            else:
+                pass
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            out.append(x)
+        return out
+
+
+class PreActResidualConvUnit(nn.Module):
+    """ResidualConvUnit, pre-activate residual unit.
+    Args:
+        in_channels (int): number of channels in the input feature map.
+        act_layer (nn.Module): activation layer.
+        norm_layer (nn.Module): norm layer.
+        stride (int): stride of the first block. Default: 1
+        dilation (int): dilation rate for convs layers. Default: 1.
+    """
+
+    def __init__(self, in_channels, act_layer, norm_layer, stride=1, dilation=1):
+        super(PreActResidualConvUnit, self).__init__()
+
+        self.conv1 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            norm_layer=norm_layer,
+            act_layer=act_layer,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+        self.conv2 = ConvModule(
+            in_channels,
+            in_channels,
+            3,
+            padding=1,
+            norm_layer=norm_layer,
+            act_layer=act_layer,
+            bias=False,
+            order=("act", "conv", "norm"),
+        )
+
+    def forward(self, inputs):
+        inputs_ = inputs.clone()
+        x = self.conv1(inputs)
+        x = self.conv2(x)
+        return x + inputs_
+
+
+class FeatureFusionBlock(nn.Module):
+    """FeatureFusionBlock, merge feature map from different stages.
+    Args:
+        in_channels (int): Input channels.
+        act_layer (nn.Module): activation layer for ResidualConvUnit.
+        norm_layer (nn.Module): normalization layer.
+        expand (bool): Whether expand the channels in post process block.
+            Default: False.
+        align_corners (bool): align_corner setting for bilinear upsample.
+            Default: True.
+    """
+
+    def __init__(self, in_channels, act_layer, norm_layer, expand=False, align_corners=True):
+        super(FeatureFusionBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.expand = expand
+        self.align_corners = align_corners
+
+        self.out_channels = in_channels
+        if self.expand:
+            self.out_channels = in_channels // 2
+
+        self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_layer=None, bias=True)
+
+        self.res_conv_unit1 = PreActResidualConvUnit(
+            in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
+        )
+        self.res_conv_unit2 = PreActResidualConvUnit(
+            in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer
+        )
+
+    def forward(self, *inputs):
+        x = inputs[0]
+        if len(inputs) == 2:
+            if x.shape != inputs[1].shape:
+                res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False)
+            else:
+                res = inputs[1]
+            x = x + self.res_conv_unit1(res)
+        x = self.res_conv_unit2(x)
+        x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
+        x = self.project(x)
+        return x
+
+
+class DPTHead(DepthBaseDecodeHead):
+    """Vision Transformers for Dense Prediction.
+    This head is implemented of `DPT <https://arxiv.org/abs/2103.13413>`_.
+    Args:
+        embed_dims (int): The embed dimension of the ViT backbone.
+            Default: 768.
+        post_process_channels (List): Out channels of post process conv
+            layers. Default: [96, 192, 384, 768].
+        readout_type (str): Type of readout operation. Default: 'ignore'.
+        patch_size (int): The patch size. Default: 16.
+        expand_channels (bool): Whether expand the channels in post process
+            block. Default: False.
+    """
+
+    def __init__(
+        self,
+        embed_dims=768,
+        post_process_channels=[96, 192, 384, 768],
+        readout_type="ignore",
+        patch_size=16,
+        expand_channels=False,
+        **kwargs,
+    ):
+        super(DPTHead, self).__init__(**kwargs)
+
+        self.in_channels = self.in_channels
+        self.expand_channels = expand_channels
+        self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size)
+
+        self.post_process_channels = [
+            channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels)
+        ]
+        self.convs = nn.ModuleList()
+        for channel in self.post_process_channels:
+            self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_layer=None, bias=False))
+        self.fusion_blocks = nn.ModuleList()
+        for _ in range(len(self.convs)):
+            self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_layer, self.norm_layer))
+        self.fusion_blocks[0].res_conv_unit1 = None
+        self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_layer=self.norm_layer)
+        self.num_fusion_blocks = len(self.fusion_blocks)
+        self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers)
+        self.num_post_process_channels = len(self.post_process_channels)
+        assert self.num_fusion_blocks == self.num_reassemble_blocks
+        assert self.num_reassemble_blocks == self.num_post_process_channels
+        self.conv_depth = HeadDepth(self.channels)
+
+    def forward(self, inputs, img_metas):
+        assert len(inputs) == self.num_reassemble_blocks
+        x = [inp for inp in inputs]
+        x = self.reassemble_blocks(x)
+        x = [self.convs[i](feature) for i, feature in enumerate(x)]
+        out = self.fusion_blocks[0](x[-1])
+        for i in range(1, len(self.fusion_blocks)):
+            out = self.fusion_blocks[i](out, x[-(i + 1)])
+        out = self.project(out)
+        out = self.depth_pred(out)
+        return out
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/encoder_decoder.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/encoder_decoder.py
new file mode 100644
index 0000000..eb29ced
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/encoder_decoder.py
@@ -0,0 +1,351 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .ops import resize
+
+
+def add_prefix(inputs, prefix):
+    """Add prefix for dict.
+
+    Args:
+        inputs (dict): The input dict with str keys.
+        prefix (str): The prefix to add.
+
+    Returns:
+
+        dict: The dict with keys updated with ``prefix``.
+    """
+
+    outputs = dict()
+    for name, value in inputs.items():
+        outputs[f"{prefix}.{name}"] = value
+
+    return outputs
+
+
+class DepthEncoderDecoder(nn.Module):
+    """Encoder Decoder depther.
+
+    EncoderDecoder typically consists of backbone and decode_head.
+    """
+
+    def __init__(self, backbone, decode_head):
+        super(DepthEncoderDecoder, self).__init__()
+
+        self.backbone = backbone
+        self.decode_head = decode_head
+        self.align_corners = self.decode_head.align_corners
+
+    def extract_feat(self, img):
+        """Extract features from images."""
+        return self.backbone(img)
+
+    def encode_decode(self, img, img_metas, rescale=True, size=None):
+        """Encode images with backbone and decode into a depth estimation
+        map of the same size as input."""
+        x = self.extract_feat(img)
+        out = self._decode_head_forward_test(x, img_metas)
+        # crop the pred depth to the certain range.
+        out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth)
+        if rescale:
+            if size is None:
+                if img_metas is not None:
+                    size = img_metas[0]["ori_shape"][:2]
+                else:
+                    size = img.shape[2:]
+            out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners)
+        return out
+
+    def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs):
+        """Run forward function and calculate loss for decode head in
+        training."""
+        losses = dict()
+        loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, **kwargs)
+        losses.update(add_prefix(loss_decode, "decode"))
+        return losses
+
+    def _decode_head_forward_test(self, x, img_metas):
+        """Run forward function and calculate loss for decode head in
+        inference."""
+        depth_pred = self.decode_head.forward_test(x, img_metas)
+        return depth_pred
+
+    def forward_dummy(self, img):
+        """Dummy forward function."""
+        depth = self.encode_decode(img, None)
+
+        return depth
+
+    def forward_train(self, img, img_metas, depth_gt, **kwargs):
+        """Forward function for training.
+
+        Args:
+            img (Tensor): Input images.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            depth_gt (Tensor): Depth gt
+                used if the architecture supports depth estimation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+
+        x = self.extract_feat(img)
+
+        losses = dict()
+
+        # the last of x saves the info from neck
+        loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs)
+
+        losses.update(loss_decode)
+
+        return losses
+
+    def whole_inference(self, img, img_meta, rescale, size=None):
+        """Inference with full image."""
+        return self.encode_decode(img, img_meta, rescale, size=size)
+
+    def slide_inference(self, img, img_meta, rescale, stride, crop_size):
+        """Inference by sliding-window with overlap.
+
+        If h_crop > h_img or w_crop > w_img, the small patch will be used to
+        decode without padding.
+        """
+
+        h_stride, w_stride = stride
+        h_crop, w_crop = crop_size
+        batch_size, _, h_img, w_img = img.size()
+        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
+        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
+        preds = img.new_zeros((batch_size, 1, h_img, w_img))
+        count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
+        for h_idx in range(h_grids):
+            for w_idx in range(w_grids):
+                y1 = h_idx * h_stride
+                x1 = w_idx * w_stride
+                y2 = min(y1 + h_crop, h_img)
+                x2 = min(x1 + w_crop, w_img)
+                y1 = max(y2 - h_crop, 0)
+                x1 = max(x2 - w_crop, 0)
+                crop_img = img[:, :, y1:y2, x1:x2]
+                depth_pred = self.encode_decode(crop_img, img_meta, rescale)
+                preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2)))
+
+                count_mat[:, :, y1:y2, x1:x2] += 1
+        assert (count_mat == 0).sum() == 0
+        if torch.onnx.is_in_onnx_export():
+            # cast count_mat to constant while exporting to ONNX
+            count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device)
+        preds = preds / count_mat
+        return preds
+
+    def inference(self, img, img_meta, rescale, size=None, mode="whole"):
+        """Inference with slide/whole style.
+
+        Args:
+            img (Tensor): The input image of shape (N, 3, H, W).
+            img_meta (dict): Image info dict where each dict has: 'img_shape',
+                'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `depth/datasets/pipelines/formatting.py:Collect`.
+            rescale (bool): Whether rescale back to original shape.
+
+        Returns:
+            Tensor: The output depth map.
+        """
+
+        assert mode in ["slide", "whole"]
+        ori_shape = img_meta[0]["ori_shape"]
+        assert all(_["ori_shape"] == ori_shape for _ in img_meta)
+        if mode == "slide":
+            depth_pred = self.slide_inference(img, img_meta, rescale)
+        else:
+            depth_pred = self.whole_inference(img, img_meta, rescale, size=size)
+        output = depth_pred
+        flip = img_meta[0]["flip"]
+        if flip:
+            flip_direction = img_meta[0]["flip_direction"]
+            assert flip_direction in ["horizontal", "vertical"]
+            if flip_direction == "horizontal":
+                output = output.flip(dims=(3,))
+            elif flip_direction == "vertical":
+                output = output.flip(dims=(2,))
+
+        return output
+
+    def simple_test(self, img, img_meta, rescale=True):
+        """Simple test with single image."""
+        depth_pred = self.inference(img, img_meta, rescale)
+        if torch.onnx.is_in_onnx_export():
+            # our inference backend only support 4D output
+            depth_pred = depth_pred.unsqueeze(0)
+            return depth_pred
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def aug_test(self, imgs, img_metas, rescale=True):
+        """Test with augmentations.
+
+        Only rescale=True is supported.
+        """
+        # aug_test rescale all imgs back to ori_shape for now
+        assert rescale
+        # to save memory, we get augmented depth logit inplace
+        depth_pred = self.inference(imgs[0], img_metas[0], rescale)
+        for i in range(1, len(imgs)):
+            cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:])
+            depth_pred += cur_depth_pred
+        depth_pred /= len(imgs)
+        depth_pred = depth_pred.cpu().numpy()
+        # unravel batch dim
+        depth_pred = list(depth_pred)
+        return depth_pred
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+        """
+        for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]:
+            if not isinstance(var, list):
+                raise TypeError(f"{name} must be a list, but got " f"{type(var)}")
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})")
+        # all images in the same aug batch all of the same ori_shape and pad
+        # shape
+        for img_meta in img_metas:
+            ori_shapes = [_["ori_shape"] for _ in img_meta]
+            assert all(shape == ori_shapes[0] for shape in ori_shapes)
+            img_shapes = [_["img_shape"] for _ in img_meta]
+            assert all(shape == img_shapes[0] for shape in img_shapes)
+            pad_shapes = [_["pad_shape"] for _ in img_meta]
+            assert all(shape == pad_shapes[0] for shape in pad_shapes)
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], **kwargs)
+        else:
+            return self.aug_test(imgs, img_metas, **kwargs)
+
+    def forward(self, img, img_metas, return_loss=True, **kwargs):
+        """Calls either :func:`forward_train` or :func:`forward_test` depending
+        on whether ``return_loss`` is ``True``.
+
+        Note this setting will change the expected inputs. When
+        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
+        and List[dict]), and when ``resturn_loss=False``, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if return_loss:
+            return self.forward_train(img, img_metas, **kwargs)
+        else:
+            return self.forward_test(img, img_metas, **kwargs)
+
+    def train_step(self, data_batch, optimizer, **kwargs):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating is also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+                ``num_samples``.
+                ``loss`` is a tensor for back propagation, which can be a
+                weighted sum of multiple losses.
+                ``log_vars`` contains all the variables to be sent to the
+                logger.
+                ``num_samples`` indicates the batch size (when the model is
+                DDP, it means the batch size on each GPU), which is used for
+                averaging the logs.
+        """
+        losses = self(**data_batch)
+
+        # split losses and images
+        real_losses = {}
+        log_imgs = {}
+        for k, v in losses.items():
+            if "img" in k:
+                log_imgs[k] = v
+            else:
+                real_losses[k] = v
+
+        loss, log_vars = self._parse_losses(real_losses)
+
+        outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs)
+
+        return outputs
+
+    def val_step(self, data_batch, **kwargs):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        output = self(**data_batch, **kwargs)
+        return output
+
+    @staticmethod
+    def _parse_losses(losses):
+        import torch.distributed as dist
+
+        """Parse the raw outputs (losses) of the network.
+
+        Args:
+            losses (dict): Raw output of the network, which usually contain
+                losses and other necessary information.
+
+        Returns:
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
+                which may be a weighted sum of all losses, log_vars contains
+                all the variables to be sent to the logger.
+        """
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            else:
+                raise TypeError(f"{loss_name} is not a tensor or list of tensors")
+
+        loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key)
+
+        log_vars["loss"] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/ops.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/ops.py
new file mode 100644
index 0000000..15880ee
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depth/ops.py
@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import warnings
+
+import torch.nn.functional as F
+
+
+def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False):
+    if warning:
+        if size is not None and align_corners:
+            input_h, input_w = tuple(int(x) for x in input.shape[2:])
+            output_h, output_w = tuple(int(x) for x in size)
+            if output_h > input_h or output_w > output_h:
+                if (
+                    (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1)
+                    and (output_h - 1) % (input_h - 1)
+                    and (output_w - 1) % (input_w - 1)
+                ):
+                    warnings.warn(
+                        f"When align_corners={align_corners}, "
+                        "the output would more aligned if "
+                        f"input size {(input_h, input_w)} is `x+1` and "
+                        f"out size {(output_h, output_w)} is `nx+1`"
+                    )
+    return F.interpolate(input, size, scale_factor, mode, align_corners)
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depthers.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depthers.py
new file mode 100644
index 0000000..f88b7e9
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/depthers.py
@@ -0,0 +1,246 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from functools import partial
+from typing import Optional, Tuple, Union
+
+import torch
+
+from .backbones import _make_dinov2_model
+from .depth import BNHead, DepthEncoderDecoder, DPTHead
+from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name, CenterPadding
+
+
+class Weights(Enum):
+    NYU = "NYU"
+    KITTI = "KITTI"
+
+
+def _get_depth_range(pretrained: bool, weights: Weights = Weights.NYU) -> Tuple[float, float]:
+    if not pretrained:  # Default
+        return (0.001, 10.0)
+
+    # Pretrained, set according to the training dataset for the provided weights
+    if weights == Weights.KITTI:
+        return (0.001, 80.0)
+
+    if weights == Weights.NYU:
+        return (0.001, 10.0)
+
+    return (0.001, 10.0)
+
+
+def _make_dinov2_linear_depth_head(
+    *,
+    embed_dim: int,
+    layers: int,
+    min_depth: float,
+    max_depth: float,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+
+    if layers == 1:
+        in_index = [0]
+    else:
+        assert layers == 4
+        in_index = [0, 1, 2, 3]
+
+    return BNHead(
+        classify=True,
+        n_bins=256,
+        bins_strategy="UD",
+        norm_strategy="linear",
+        upsample=4,
+        in_channels=[embed_dim] * len(in_index),
+        in_index=in_index,
+        input_transform="resize_concat",
+        channels=embed_dim * len(in_index) * 2,
+        align_corners=False,
+        min_depth=0.001,
+        max_depth=80,
+        loss_decode=(),
+    )
+
+
+def _make_dinov2_linear_depther(
+    *,
+    arch_name: str = "vit_large",
+    layers: int = 4,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.NYU,
+    depth_range: Optional[Tuple[float, float]] = None,
+    **kwargs,
+):
+    if layers not in (1, 4):
+        raise AssertionError(f"Unsupported number of layers: {layers}")
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    if depth_range is None:
+        depth_range = _get_depth_range(pretrained, weights)
+    min_depth, max_depth = depth_range
+
+    backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
+
+    embed_dim = backbone.embed_dim
+    patch_size = backbone.patch_size
+    model_name = _make_dinov2_model_name(arch_name, patch_size)
+    linear_depth_head = _make_dinov2_linear_depth_head(
+        embed_dim=embed_dim,
+        layers=layers,
+        min_depth=min_depth,
+        max_depth=max_depth,
+    )
+
+    layer_count = {
+        "vit_small": 12,
+        "vit_base": 12,
+        "vit_large": 24,
+        "vit_giant2": 40,
+    }[arch_name]
+
+    if layers == 4:
+        out_index = {
+            "vit_small": [2, 5, 8, 11],
+            "vit_base": [2, 5, 8, 11],
+            "vit_large": [4, 11, 17, 23],
+            "vit_giant2": [9, 19, 29, 39],
+        }[arch_name]
+    else:
+        assert layers == 1
+        out_index = [layer_count - 1]
+
+    model = DepthEncoderDecoder(backbone=backbone, decode_head=linear_depth_head)
+    model.backbone.forward = partial(
+        backbone.get_intermediate_layers,
+        n=out_index,
+        reshape=True,
+        return_class_token=True,
+        norm=False,
+    )
+    model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(patch_size)(x[0]))
+
+    if pretrained:
+        layers_str = str(layers) if layers == 4 else ""
+        weights_str = weights.value.lower()
+        url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_linear{layers_str}_head.pth"
+        checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        if "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+        model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+def dinov2_vits14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def dinov2_vitb14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def dinov2_vitl14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def dinov2_vitg14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_linear_depther(
+        arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
+    )
+
+
+def _make_dinov2_dpt_depth_head(*, embed_dim: int, min_depth: float, max_depth: float):
+    return DPTHead(
+        in_channels=[embed_dim] * 4,
+        channels=256,
+        embed_dims=embed_dim,
+        post_process_channels=[embed_dim // 2 ** (3 - i) for i in range(4)],
+        readout_type="project",
+        min_depth=min_depth,
+        max_depth=max_depth,
+        loss_decode=(),
+    )
+
+
+def _make_dinov2_dpt_depther(
+    *,
+    arch_name: str = "vit_large",
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.NYU,
+    depth_range: Optional[Tuple[float, float]] = None,
+    **kwargs,
+):
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    if depth_range is None:
+        depth_range = _get_depth_range(pretrained, weights)
+    min_depth, max_depth = depth_range
+
+    backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
+
+    model_name = _make_dinov2_model_name(arch_name, backbone.patch_size)
+    dpt_depth_head = _make_dinov2_dpt_depth_head(embed_dim=backbone.embed_dim, min_depth=min_depth, max_depth=max_depth)
+
+    out_index = {
+        "vit_small": [2, 5, 8, 11],
+        "vit_base": [2, 5, 8, 11],
+        "vit_large": [4, 11, 17, 23],
+        "vit_giant2": [9, 19, 29, 39],
+    }[arch_name]
+
+    model = DepthEncoderDecoder(backbone=backbone, decode_head=dpt_depth_head)
+    model.backbone.forward = partial(
+        backbone.get_intermediate_layers,
+        n=out_index,
+        reshape=True,
+        return_class_token=True,
+        norm=False,
+    )
+    model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone.patch_size)(x[0]))
+
+    if pretrained:
+        weights_str = weights.value.lower()
+        url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_dpt_head.pth"
+        checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        if "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+        model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+def dinov2_vits14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitb14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitl14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitg14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
+    return _make_dinov2_dpt_depther(
+        arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
+    )
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/utils.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/utils.py
new file mode 100644
index 0000000..9c66414
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/hub/utils.py
@@ -0,0 +1,39 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import itertools
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
+
+
+def _make_dinov2_model_name(arch_name: str, patch_size: int, num_register_tokens: int = 0) -> str:
+    compact_arch_name = arch_name.replace("_", "")[:4]
+    registers_suffix = f"_reg{num_register_tokens}" if num_register_tokens else ""
+    return f"dinov2_{compact_arch_name}{patch_size}{registers_suffix}"
+
+
+class CenterPadding(nn.Module):
+    def __init__(self, multiple):
+        super().__init__()
+        self.multiple = multiple
+
+    def _get_pad(self, size):
+        new_size = math.ceil(size / self.multiple) * self.multiple
+        pad_size = new_size - size
+        pad_size_left = pad_size // 2
+        pad_size_right = pad_size - pad_size_left
+        return pad_size_left, pad_size_right
+
+    @torch.inference_mode()
+    def forward(self, x):
+        pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))
+        output = F.pad(x, pads)
+        return output
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/__init__.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/__init__.py
new file mode 100644
index 0000000..05a0b61
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from .dino_head import DINOHead
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .attention import MemEffAttention
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/attention.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/attention.py
new file mode 100644
index 0000000..0fb76ef
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/attention.py
@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+import os
+import warnings
+
+from torch import Tensor
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import memory_efficient_attention, unbind
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Attention)")
+    else:
+        warnings.warn("xFormers is disabled (Attention)")
+        raise ImportError
+except ImportError:
+    XFORMERS_AVAILABLE = False
+    warnings.warn("xFormers is not available (Attention)")
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            if attn_bias is not None:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/block.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/block.py
new file mode 100644
index 0000000..930787b
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/block.py
@@ -0,0 +1,260 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+import os
+from typing import Callable, List, Any, Tuple, Dict
+import warnings
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import fmha, scaled_index_add, index_select_cat
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Block)")
+    else:
+        warnings.warn("xFormers is disabled (Block)")
+        raise ImportError
+except ImportError:
+    XFORMERS_AVAILABLE = False
+
+    warnings.warn("xFormers is not available (Block)")
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+
+    def forward(self, x_or_x_list):
+        if isinstance(x_or_x_list, Tensor):
+            return super().forward(x_or_x_list)
+        elif isinstance(x_or_x_list, list):
+            if not XFORMERS_AVAILABLE:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/dino_head.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/dino_head.py
new file mode 100644
index 0000000..0ace8ff
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/dino_head.py
@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.nn.init import trunc_normal_
+from torch.nn.utils import weight_norm
+
+
+class DINOHead(nn.Module):
+    def __init__(
+        self,
+        in_dim,
+        out_dim,
+        use_bn=False,
+        nlayers=3,
+        hidden_dim=2048,
+        bottleneck_dim=256,
+        mlp_bias=True,
+    ):
+        super().__init__()
+        nlayers = max(nlayers, 1)
+        self.mlp = _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=hidden_dim, use_bn=use_bn, bias=mlp_bias)
+        self.apply(self._init_weights)
+        self.last_layer = weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
+        self.last_layer.weight_g.data.fill_(1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.mlp(x)
+        eps = 1e-6 if x.dtype == torch.float16 else 1e-12
+        x = nn.functional.normalize(x, dim=-1, p=2, eps=eps)
+        x = self.last_layer(x)
+        return x
+
+
+def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True):
+    if nlayers == 1:
+        return nn.Linear(in_dim, bottleneck_dim, bias=bias)
+    else:
+        layers = [nn.Linear(in_dim, hidden_dim, bias=bias)]
+        if use_bn:
+            layers.append(nn.BatchNorm1d(hidden_dim))
+        layers.append(nn.GELU())
+        for _ in range(nlayers - 2):
+            layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias))
+            if use_bn:
+                layers.append(nn.BatchNorm1d(hidden_dim))
+            layers.append(nn.GELU())
+        layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias))
+        return nn.Sequential(*layers)
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/drop_path.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/drop_path.py
new file mode 100644
index 0000000..1d640e0
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/drop_path.py
@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - 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:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/layer_scale.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/layer_scale.py
new file mode 100644
index 0000000..0b38971
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/layer_scale.py
@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Optional, Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.init_values = init_values
+        self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.constant_(self.gamma, self.init_values)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/mlp.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/mlp.py
new file mode 100644
index 0000000..bbf9432
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/mlp.py
@@ -0,0 +1,40 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
+
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/patch_embed.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/patch_embed.py
new file mode 100644
index 0000000..8b7c080
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/patch_embed.py
@@ -0,0 +1,88 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/swiglu_ffn.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/swiglu_ffn.py
new file mode 100644
index 0000000..5e9dafa
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/layers/swiglu_ffn.py
@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import os
+from typing import Callable, Optional
+import warnings
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import SwiGLU
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (SwiGLU)")
+    else:
+        warnings.warn("xFormers is disabled (SwiGLU)")
+        raise ImportError
+except ImportError:
+    SwiGLU = SwiGLUFFN
+    XFORMERS_AVAILABLE = False
+
+    warnings.warn("xFormers is not available (SwiGLU)")
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/models/__init__.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/models/__init__.py
new file mode 100644
index 0000000..3fdff20
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/models/__init__.py
@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+
+from . import vision_transformer as vits
+
+
+logger = logging.getLogger("dinov2")
+
+
+def build_model(args, only_teacher=False, img_size=224):
+    args.arch = args.arch.removesuffix("_memeff")
+    if "vit" in args.arch:
+        vit_kwargs = dict(
+            img_size=img_size,
+            patch_size=args.patch_size,
+            init_values=args.layerscale,
+            ffn_layer=args.ffn_layer,
+            block_chunks=args.block_chunks,
+            qkv_bias=args.qkv_bias,
+            proj_bias=args.proj_bias,
+            ffn_bias=args.ffn_bias,
+            num_register_tokens=args.num_register_tokens,
+            interpolate_offset=args.interpolate_offset,
+            interpolate_antialias=args.interpolate_antialias,
+        )
+        teacher = vits.__dict__[args.arch](**vit_kwargs)
+        if only_teacher:
+            return teacher, teacher.embed_dim
+        student = vits.__dict__[args.arch](
+            **vit_kwargs,
+            drop_path_rate=args.drop_path_rate,
+            drop_path_uniform=args.drop_path_uniform,
+        )
+        embed_dim = student.embed_dim
+    return student, teacher, embed_dim
+
+
+def build_model_from_cfg(cfg, only_teacher=False):
+    return build_model(cfg.student, only_teacher=only_teacher, img_size=cfg.crops.global_crops_size)
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/models/vision_transformer.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/models/vision_transformer.py
new file mode 100644
index 0000000..74df767
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/models/vision_transformer.py
@@ -0,0 +1,397 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = np.linspace(0, drop_path_rate, depth).tolist()  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        M = int(math.sqrt(N))  # Recover the number of patches in each dimension
+        assert N == M * M
+        kwargs = {}
+        if self.interpolate_offset:
+            # Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8
+            # Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors
+            sx = float(w0 + self.interpolate_offset) / M
+            sy = float(h0 + self.interpolate_offset) / M
+            kwargs["scale_factor"] = (sx, sy)
+        else:
+            # Simply specify an output size instead of a scale factor
+            kwargs["size"] = (w0, h0)
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, M, M, dim).permute(0, 3, 1, 2),
+            mode="bicubic",
+            antialias=self.interpolate_antialias,
+            **kwargs,
+        )
+        assert (w0, h0) == patch_pos_embed.shape[-2:]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list):
+        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+
+        x = self.prepare_tokens_with_masks(x, masks)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens :] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/__init__.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/__init__.py
new file mode 100644
index 0000000..b88da6b
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/cluster.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/cluster.py
new file mode 100644
index 0000000..3df87dc
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/cluster.py
@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+
+class ClusterType(Enum):
+    AWS = "aws"
+    FAIR = "fair"
+    RSC = "rsc"
+
+
+def _guess_cluster_type() -> ClusterType:
+    uname = os.uname()
+    if uname.sysname == "Linux":
+        if uname.release.endswith("-aws"):
+            # Linux kernel versions on AWS instances are of the form "5.4.0-1051-aws"
+            return ClusterType.AWS
+        elif uname.nodename.startswith("rsc"):
+            # Linux kernel versions on RSC instances are standard ones but hostnames start with "rsc"
+            return ClusterType.RSC
+
+    return ClusterType.FAIR
+
+
+def get_cluster_type(cluster_type: Optional[ClusterType] = None) -> Optional[ClusterType]:
+    if cluster_type is None:
+        return _guess_cluster_type()
+
+    return cluster_type
+
+
+def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type is None:
+        return None
+
+    CHECKPOINT_DIRNAMES = {
+        ClusterType.AWS: "checkpoints",
+        ClusterType.FAIR: "checkpoint",
+        ClusterType.RSC: "checkpoint/dino",
+    }
+    return Path("/") / CHECKPOINT_DIRNAMES[cluster_type]
+
+
+def get_user_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
+    checkpoint_path = get_checkpoint_path(cluster_type)
+    if checkpoint_path is None:
+        return None
+
+    username = os.environ.get("USER")
+    assert username is not None
+    return checkpoint_path / username
+
+
+def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]:
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type is None:
+        return None
+
+    SLURM_PARTITIONS = {
+        ClusterType.AWS: "learnlab",
+        ClusterType.FAIR: "learnlab",
+        ClusterType.RSC: "learn",
+    }
+    return SLURM_PARTITIONS[cluster_type]
+
+
+def get_slurm_executor_parameters(
+    nodes: int, num_gpus_per_node: int, cluster_type: Optional[ClusterType] = None, **kwargs
+) -> Dict[str, Any]:
+    # create default parameters
+    params = {
+        "mem_gb": 0,  # Requests all memory on a node, see https://slurm.schedmd.com/sbatch.html
+        "gpus_per_node": num_gpus_per_node,
+        "tasks_per_node": num_gpus_per_node,  # one task per GPU
+        "cpus_per_task": 10,
+        "nodes": nodes,
+        "slurm_partition": get_slurm_partition(cluster_type),
+    }
+    # apply cluster-specific adjustments
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type == ClusterType.AWS:
+        params["cpus_per_task"] = 12
+        del params["mem_gb"]
+    elif cluster_type == ClusterType.RSC:
+        params["cpus_per_task"] = 12
+    # set additional parameters / apply overrides
+    params.update(kwargs)
+    return params
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/config.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/config.py
new file mode 100644
index 0000000..c9de578
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/config.py
@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import math
+import logging
+import os
+
+from omegaconf import OmegaConf
+
+import dinov2.distributed as distributed
+from dinov2.logging import setup_logging
+from dinov2.utils import utils
+from dinov2.configs import dinov2_default_config
+
+
+logger = logging.getLogger("dinov2")
+
+
+def apply_scaling_rules_to_cfg(cfg):  # to fix
+    if cfg.optim.scaling_rule == "sqrt_wrt_1024":
+        base_lr = cfg.optim.base_lr
+        cfg.optim.lr = base_lr
+        cfg.optim.lr *= math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_global_size() / 1024.0)
+        logger.info(f"sqrt scaling learning rate; base: {base_lr}, new: {cfg.optim.lr}")
+    else:
+        raise NotImplementedError
+    return cfg
+
+
+def write_config(cfg, output_dir, name="config.yaml"):
+    logger.info(OmegaConf.to_yaml(cfg))
+    saved_cfg_path = os.path.join(output_dir, name)
+    with open(saved_cfg_path, "w") as f:
+        OmegaConf.save(config=cfg, f=f)
+    return saved_cfg_path
+
+
+def get_cfg_from_args(args):
+    args.output_dir = os.path.abspath(args.output_dir)
+    args.opts += [f"train.output_dir={args.output_dir}"]
+    default_cfg = OmegaConf.create(dinov2_default_config)
+    cfg = OmegaConf.load(args.config_file)
+    cfg = OmegaConf.merge(default_cfg, cfg, OmegaConf.from_cli(args.opts))
+    return cfg
+
+
+def default_setup(args):
+    distributed.enable(overwrite=True)
+    seed = getattr(args, "seed", 0)
+    rank = distributed.get_global_rank()
+
+    global logger
+    setup_logging(output=args.output_dir, level=logging.INFO)
+    logger = logging.getLogger("dinov2")
+
+    utils.fix_random_seeds(seed + rank)
+    logger.info("git:\n  {}\n".format(utils.get_sha()))
+    logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
+
+
+def setup(args):
+    """
+    Create configs and perform basic setups.
+    """
+    cfg = get_cfg_from_args(args)
+    os.makedirs(args.output_dir, exist_ok=True)
+    default_setup(args)
+    apply_scaling_rules_to_cfg(cfg)
+    write_config(cfg, args.output_dir)
+    return cfg
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/dtype.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/dtype.py
new file mode 100644
index 0000000..80f4cd7
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/dtype.py
@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+
+from typing import Dict, Union
+
+import numpy as np
+import torch
+
+
+TypeSpec = Union[str, np.dtype, torch.dtype]
+
+
+_NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = {
+    np.dtype("bool"): torch.bool,
+    np.dtype("uint8"): torch.uint8,
+    np.dtype("int8"): torch.int8,
+    np.dtype("int16"): torch.int16,
+    np.dtype("int32"): torch.int32,
+    np.dtype("int64"): torch.int64,
+    np.dtype("float16"): torch.float16,
+    np.dtype("float32"): torch.float32,
+    np.dtype("float64"): torch.float64,
+    np.dtype("complex64"): torch.complex64,
+    np.dtype("complex128"): torch.complex128,
+}
+
+
+def as_torch_dtype(dtype: TypeSpec) -> torch.dtype:
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    if isinstance(dtype, str):
+        dtype = np.dtype(dtype)
+    assert isinstance(dtype, np.dtype), f"Expected an instance of nunpy dtype, got {type(dtype)}"
+    return _NUMPY_TO_TORCH_DTYPE[dtype]
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/param_groups.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/param_groups.py
new file mode 100644
index 0000000..9a5d2ff
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/param_groups.py
@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from collections import defaultdict
+import logging
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False):
+    """
+    Calculate lr decay rate for different ViT blocks.
+    Args:
+        name (string): parameter name.
+        lr_decay_rate (float): base lr decay rate.
+        num_layers (int): number of ViT blocks.
+    Returns:
+        lr decay rate for the given parameter.
+    """
+    layer_id = num_layers + 1
+    if name.startswith("backbone") or force_is_backbone:
+        if (
+            ".pos_embed" in name
+            or ".patch_embed" in name
+            or ".mask_token" in name
+            or ".cls_token" in name
+            or ".register_tokens" in name
+        ):
+            layer_id = 0
+        elif force_is_backbone and (
+            "pos_embed" in name
+            or "patch_embed" in name
+            or "mask_token" in name
+            or "cls_token" in name
+            or "register_tokens" in name
+        ):
+            layer_id = 0
+        elif ".blocks." in name and ".residual." not in name:
+            layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
+        elif chunked_blocks and "blocks." in name and "residual." not in name:
+            layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1
+        elif "blocks." in name and "residual." not in name:
+            layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1
+
+    return lr_decay_rate ** (num_layers + 1 - layer_id)
+
+
+def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0):
+    chunked_blocks = False
+    if hasattr(model, "n_blocks"):
+        logger.info("chunked fsdp")
+        n_blocks = model.n_blocks
+        chunked_blocks = model.chunked_blocks
+    elif hasattr(model, "blocks"):
+        logger.info("first code branch")
+        n_blocks = len(model.blocks)
+    elif hasattr(model, "backbone"):
+        logger.info("second code branch")
+        n_blocks = len(model.backbone.blocks)
+    else:
+        logger.info("else code branch")
+        n_blocks = 0
+    all_param_groups = []
+
+    for name, param in model.named_parameters():
+        name = name.replace("_fsdp_wrapped_module.", "")
+        if not param.requires_grad:
+            continue
+        decay_rate = get_vit_lr_decay_rate(
+            name, lr_decay_rate, num_layers=n_blocks, force_is_backbone=n_blocks > 0, chunked_blocks=chunked_blocks
+        )
+        d = {"params": param, "is_last_layer": False, "lr_multiplier": decay_rate, "wd_multiplier": 1.0, "name": name}
+
+        if "last_layer" in name:
+            d.update({"is_last_layer": True})
+
+        if name.endswith(".bias") or "norm" in name or "gamma" in name:
+            d.update({"wd_multiplier": 0.0})
+
+        if "patch_embed" in name:
+            d.update({"lr_multiplier": d["lr_multiplier"] * patch_embed_lr_mult})
+
+        all_param_groups.append(d)
+        logger.info(f"""{name}: lr_multiplier: {d["lr_multiplier"]}, wd_multiplier: {d["wd_multiplier"]}""")
+
+    return all_param_groups
+
+
+def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")):
+    fused_params_groups = defaultdict(lambda: {"params": []})
+    for d in all_params_groups:
+        identifier = ""
+        for k in keys:
+            identifier += k + str(d[k]) + "_"
+
+        for k in keys:
+            fused_params_groups[identifier][k] = d[k]
+        fused_params_groups[identifier]["params"].append(d["params"])
+
+    return fused_params_groups.values()
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/utils.py b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/utils.py
new file mode 100644
index 0000000..68f8e2c
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/dinov2/utils/utils.py
@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import random
+import subprocess
+from urllib.parse import urlparse
+
+import numpy as np
+import torch
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+def load_pretrained_weights(model, pretrained_weights, checkpoint_key):
+    if urlparse(pretrained_weights).scheme:  # If it looks like an URL
+        state_dict = torch.hub.load_state_dict_from_url(pretrained_weights, map_location="cpu")
+    else:
+        state_dict = torch.load(pretrained_weights, map_location="cpu")
+    if checkpoint_key is not None and checkpoint_key in state_dict:
+        logger.info(f"Take key {checkpoint_key} in provided checkpoint dict")
+        state_dict = state_dict[checkpoint_key]
+    # remove `module.` prefix
+    state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+    # remove `backbone.` prefix induced by multicrop wrapper
+    state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
+    msg = model.load_state_dict(state_dict, strict=False)
+    logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg))
+
+
+def fix_random_seeds(seed=31):
+    """
+    Fix random seeds.
+    """
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+    sha = "N/A"
+    diff = "clean"
+    branch = "N/A"
+    try:
+        sha = _run(["git", "rev-parse", "HEAD"])
+        subprocess.check_output(["git", "diff"], cwd=cwd)
+        diff = _run(["git", "diff-index", "HEAD"])
+        diff = "has uncommitted changes" if diff else "clean"
+        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+class CosineScheduler(object):
+    def __init__(self, base_value, final_value, total_iters, warmup_iters=0, start_warmup_value=0, freeze_iters=0):
+        super().__init__()
+        self.final_value = final_value
+        self.total_iters = total_iters
+
+        freeze_schedule = np.zeros((freeze_iters))
+
+        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
+
+        iters = np.arange(total_iters - warmup_iters - freeze_iters)
+        schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters)))
+        self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule))
+
+        assert len(self.schedule) == self.total_iters
+
+    def __getitem__(self, it):
+        if it >= self.total_iters:
+            return self.final_value
+        else:
+            return self.schedule[it]
+
+
+def has_batchnorms(model):
+    bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
+    for name, module in model.named_modules():
+        if isinstance(module, bn_types):
+            return True
+    return False
diff --git a/Gen_3D_Modules/Stable3DGen/dinov2/hubconf.py b/Gen_3D_Modules/Stable3DGen/dinov2/hubconf.py
new file mode 100644
index 0000000..d3664e2
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/dinov2/hubconf.py
@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+
+from dinov2.hub.backbones import dinov2_vitb14, dinov2_vitg14, dinov2_vitl14, dinov2_vits14
+from dinov2.hub.backbones import dinov2_vitb14_reg, dinov2_vitg14_reg, dinov2_vitl14_reg, dinov2_vits14_reg
+from dinov2.hub.classifiers import dinov2_vitb14_lc, dinov2_vitg14_lc, dinov2_vitl14_lc, dinov2_vits14_lc
+from dinov2.hub.classifiers import dinov2_vitb14_reg_lc, dinov2_vitg14_reg_lc, dinov2_vitl14_reg_lc, dinov2_vits14_reg_lc
+from dinov2.hub.depthers import dinov2_vitb14_ld, dinov2_vitg14_ld, dinov2_vitl14_ld, dinov2_vits14_ld
+from dinov2.hub.depthers import dinov2_vitb14_dd, dinov2_vitg14_dd, dinov2_vitl14_dd, dinov2_vits14_dd
+
+
+dependencies = ["torch"]
diff --git a/Gen_3D_Modules/Stable3DGen/pipeline_builders.py b/Gen_3D_Modules/Stable3DGen/pipeline_builders.py
new file mode 100644
index 0000000..6649d24
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/pipeline_builders.py
@@ -0,0 +1,388 @@
+import os
+import json
+import torch
+import shutil
+from accelerate import init_empty_weights
+from accelerate.utils import set_module_tensor_to_device
+from safetensors.torch import load_file
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from huggingface_hub import snapshot_download
+
+from .stablex.pipeline_yoso import YosoPipeline
+from .stablex.controlnetvae import ControlNetVAEModel
+from .trellis.pipelines.trellis_image_to_3d import TrellisImageTo3DPipeline as Stable3DGenTrellisImageTo3DPipeline
+from .trellis.backend_config import (
+    set_attention_backend,
+    set_sparse_backend,
+    get_available_backends,
+    get_available_sparse_backends
+)
+
+DEVICE_STR = "cuda" if torch.cuda.is_available() else "cpu"
+DEVICE = torch.device(DEVICE_STR)
+HF_DOWNLOAD_IGNORE = ["*.yaml", "*.json", "*.py", ".png", ".jpg", ".gif"]
+
+
+class StableGenPipelineBuilder:
+    
+    @staticmethod
+    def get_available_backends():
+        try:
+            attn_backends = get_available_backends()
+            sparse_backends = get_available_sparse_backends()
+        except:
+            attn_backends = {'flash_attn': True, 'sdpa': True}
+            sparse_backends = {'spconv': True}
+
+        available_attn = [k for k, v in attn_backends.items() if v]
+        if not available_attn:
+            available_attn = ['flash_attn']
+
+        available_sparse = [k for k, v in sparse_backends.items() if v]
+        if not available_sparse:
+            available_sparse = ['spconv']
+            
+        return available_attn, available_sparse
+    
+    @staticmethod
+    def create_trellis_pipeline_params(
+        seed: int = 1234,
+        ss_sampling_steps: int = 12,
+        ss_guidance_strength: float = 7.5,
+        slat_sampling_steps: int = 12,
+        slat_guidance_strength: float = 3.0,
+        formats: list = None
+    ):
+        if formats is None:
+            formats = ["mesh"]
+            
+        return {
+            "num_samples": 1,
+            "seed": seed,
+            "formats": formats,
+            "preprocess_image": True,
+            "sparse_structure_sampler_params": {
+                "steps": ss_sampling_steps,
+                "cfg_strength": ss_guidance_strength,
+            },
+            "slat_sampler_params": {
+                "steps": slat_sampling_steps,
+                "cfg_strength": slat_guidance_strength,
+            }
+        }
+    
+    @staticmethod
+    def build_trellis_pipeline(
+        repo: str, 
+        dinov2_model: str, 
+        use_fp16: bool, 
+        attn_backend: str,
+        sparse_backend: str, 
+        spconv_algo: str, 
+        smooth_k: bool,
+        ckpt_path: str
+    ):
+        
+        StableGenPipelineBuilder._ensure_trellis_weights(repo, ckpt_path)
+        
+        StableGenPipelineBuilder._setup_environment(attn_backend, sparse_backend, spconv_algo, smooth_k)
+
+        model_dir = os.path.join(ckpt_path, "trellis", repo)
+
+        try:
+            pipe = Stable3DGenTrellisImageTo3DPipeline.from_pretrained(
+                model_dir,
+                dinov2_model=dinov2_model
+            )
+
+            if pipe is None:
+                raise Exception("Pipeline returned None from from_pretrained")
+
+            pipe = StableGenPipelineBuilder._optimize_trellis_pipeline(pipe, use_fp16)
+            return pipe
+            
+        except FileNotFoundError as e:
+            if "Weights file not found" in str(e):
+                print(f"Trellis weights missing, re-downloading {repo}...")
+                StableGenPipelineBuilder._ensure_trellis_weights(repo, ckpt_path)
+                
+                try:
+                    pipe = Stable3DGenTrellisImageTo3DPipeline.from_pretrained(
+                        model_dir,
+                        dinov2_model=dinov2_model
+                    )
+                    
+                    if pipe is None:
+                        raise Exception("Pipeline returned None from from_pretrained")
+
+                    pipe = StableGenPipelineBuilder._optimize_trellis_pipeline(pipe, use_fp16)
+                    return pipe
+                    
+                except Exception as retry_e:
+                    raise Exception(f"Failed to build Trellis pipeline after re-download: {retry_e}")
+            else:
+                raise Exception(f"Failed to build Trellis pipeline: {e}")
+        except Exception as e:
+            raise Exception(f"Failed to build Trellis pipeline: {e}")
+    
+    @staticmethod
+    def build_stablex_pipeline(repo: str, use_fp16: bool, ckpt_path: str):
+        
+        StableGenPipelineBuilder._ensure_stablex_weights(repo, ckpt_path)
+
+        model_dir = os.path.join(ckpt_path, "stablex", repo)
+        torch_dtype = torch.float16 if use_fp16 else torch.float32
+
+        try:
+            config_path = os.path.join(model_dir, 'unet', 'config.json')
+            
+            if not os.path.exists(config_path):
+                print(f"UNet config not found, re-downloading {repo}...")
+                StableGenPipelineBuilder._ensure_stablex_weights(repo, ckpt_path)
+                
+                if not os.path.exists(config_path):
+                    raise FileNotFoundError(f"Config not found at {config_path}")
+            
+            with open(config_path, 'r', encoding='utf-8') as file:
+                config = json.load(file)
+
+            with init_empty_weights():
+                unet = UNet2DConditionModel(**config)
+
+            unet_file_candidates = [
+                'diffusion_pytorch_model.fp16.safetensors',
+                'diffusion_pytorch_model.safetensors', 
+                'pytorch_model.fp16.safetensors',
+                'pytorch_model.safetensors'
+            ]
+            
+            unet_sd = None
+            unet_ckpt_path = None
+            
+            for filename in unet_file_candidates:
+                candidate_path = os.path.join(model_dir, 'unet', filename)
+                if os.path.exists(candidate_path):
+                    unet_ckpt_path = candidate_path
+                    print(f"Found UNet checkpoint: {unet_ckpt_path}")
+                    unet_sd = load_file(unet_ckpt_path)
+                    break
+            
+            if unet_sd is None:
+                print(f"UNet checkpoint not found, re-downloading {repo}...")
+                StableGenPipelineBuilder._ensure_stablex_weights(repo, ckpt_path)
+                
+                for filename in unet_file_candidates:
+                    candidate_path = os.path.join(model_dir, 'unet', filename)
+                    if os.path.exists(candidate_path):
+                        unet_ckpt_path = candidate_path
+                        print(f"Found UNet checkpoint after re-download: {unet_ckpt_path}")
+                        unet_sd = load_file(unet_ckpt_path)
+                        break
+                
+                if unet_sd is None:
+                    unet_dir = os.path.join(model_dir, 'unet')
+                    if os.path.exists(unet_dir):
+                        available_files = [f for f in os.listdir(unet_dir) if os.path.isfile(os.path.join(unet_dir, f))]
+                        print(f"Available files in unet directory: {available_files}")
+                    
+                    raise FileNotFoundError(f"No UNet checkpoint found in {unet_dir}. Searched for: {unet_file_candidates}")
+
+            for name, param in unet.named_parameters():
+                set_module_tensor_to_device(unet, name, device=DEVICE, dtype=torch_dtype, value=unet_sd[name])
+
+            try:
+                vae = AutoencoderKL.from_pretrained(
+                    model_dir, 
+                    subfolder="vae", 
+                    variant="fp16" if use_fp16 else None,
+                    torch_dtype=torch_dtype
+                )
+            except Exception as e:
+                print(f"Warning: Failed to load VAE with variant, trying without: {e}")
+                vae = AutoencoderKL.from_pretrained(
+                    model_dir, 
+                    subfolder="vae", 
+                    torch_dtype=torch_dtype
+                )
+            
+            try:
+                controlnet = ControlNetVAEModel.from_pretrained(
+                    model_dir, 
+                    subfolder="controlnet", 
+                    variant="fp16" if use_fp16 else None,
+                    torch_dtype=torch_dtype
+                )
+            except Exception as e:
+                print(f"Warning: Failed to load ControlNet with variant, trying without: {e}")
+                controlnet = ControlNetVAEModel.from_pretrained(
+                    model_dir, 
+                    subfolder="controlnet", 
+                    torch_dtype=torch_dtype
+                )
+
+            pipeline = YosoPipeline(
+                unet=unet,
+                vae=vae,
+                controlnet=controlnet,
+            )
+
+            pipeline.to(DEVICE)
+            
+            if torch_dtype == torch.float16 and DEVICE.type == "cuda":
+                pipeline.unet = pipeline.unet.half()
+                pipeline.vae = pipeline.vae.half()
+                pipeline.controlnet = pipeline.controlnet.half()
+            
+            return pipeline
+            
+        except Exception as e:
+            raise Exception(f"Failed to build StableX pipeline: {e}")
+    
+    @staticmethod
+    def _ensure_trellis_weights(repo: str, ckpt_path: str):
+        base_dir = os.path.join(ckpt_path, "trellis", repo)
+        
+        def check_files_exist():
+            required_files = [
+                "ckpts/ss_dec_conv3d_16l8_fp16.safetensors",
+                "ckpts/ss_flow_normal_dit_L_16l8_fp16.safetensors", 
+                "ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.safetensors",
+                "ckpts/slat_flow_normal_dit_L_64l8p2_fp16.safetensors",
+                "ckpts/ss_dec_conv3d_16l8_fp16.json",
+                "ckpts/ss_flow_normal_dit_L_16l8_fp16.json",
+                "ckpts/slat_dec_mesh_swin8_B_64l8m256c_fp16.json", 
+                "ckpts/slat_flow_normal_dit_L_64l8p2_fp16.json"
+            ]
+            
+            for req_file in required_files:
+                if not os.path.exists(os.path.join(base_dir, req_file)):
+                    print(f"Missing required file: {req_file}")
+                    return False
+                    
+            return True
+        
+        if not os.path.exists(base_dir) or not os.listdir(base_dir) or not check_files_exist():
+            print(f"Downloading {repo} to {base_dir}")
+            os.makedirs(base_dir, exist_ok=True)
+            
+            max_retries = 3
+            for attempt in range(max_retries):
+                try:
+                    print(f"Download attempt {attempt + 1}/{max_retries}")
+                    snapshot_download(
+                        repo_id=f"Stable-X/{repo}",
+                        repo_type="model",
+                        local_dir=base_dir,
+                        resume_download=True,
+                        ignore_patterns=HF_DOWNLOAD_IGNORE
+                    )
+                    
+                    if check_files_exist():
+                        print(f"Successfully downloaded {repo}")
+                        break
+                    else:
+                        print(f"Download incomplete, retrying... ({attempt + 1}/{max_retries})")
+                        
+                except Exception as e:
+                    print(f"Download failed on attempt {attempt + 1}: {e}")
+                    if attempt == max_retries - 1:
+                        raise Exception(f"Failed to download {repo} after {max_retries} attempts")
+                    
+            if not check_files_exist():
+                raise Exception(f"Failed to download required files for {repo}")
+    
+    @staticmethod
+    def _ensure_stablex_weights(repo: str, ckpt_path: str):
+        base_dir = os.path.join(ckpt_path, "stablex", repo)
+        
+        def check_files_exist():
+            required_files = [
+                "unet/config.json",
+                "vae/config.json", 
+                "controlnet/config.json",
+                "model_index.json"
+            ]
+            
+            unet_files = [
+                "unet/diffusion_pytorch_model.fp16.safetensors",
+                "unet/diffusion_pytorch_model.safetensors"
+            ]
+            
+            for req_file in required_files:
+                if not os.path.exists(os.path.join(base_dir, req_file)):
+                    print(f"Missing required file: {req_file}")
+                    return False
+            
+            unet_found = False
+            for unet_file in unet_files:
+                if os.path.exists(os.path.join(base_dir, unet_file)):
+                    unet_found = True
+                    break
+            
+            if not unet_found:
+                print(f"No UNet model file found. Checked: {unet_files}")
+                return False
+                
+            return True
+        
+        if not os.path.exists(base_dir) or not os.listdir(base_dir) or not check_files_exist():
+            print(f"Downloading {repo} to {base_dir}")
+            os.makedirs(base_dir, exist_ok=True)
+            
+            max_retries = 3
+            for attempt in range(max_retries):
+                try:
+                    print(f"Download attempt {attempt + 1}/{max_retries}")
+                    snapshot_download(
+                        repo_id=f"Stable-X/{repo}",
+                        repo_type="model",
+                        local_dir=base_dir,
+                        resume_download=True,
+                        ignore_patterns=["*text_encoder*", "tokenizer*", "*scheduler*"]
+                    )
+                    
+                    if check_files_exist():
+                        print(f"Successfully downloaded {repo}")
+                        break
+                    else:
+                        print(f"Download incomplete, retrying... ({attempt + 1}/{max_retries})")
+                        
+                except Exception as e:
+                    print(f"Download failed on attempt {attempt + 1}: {e}")
+                    if attempt == max_retries - 1:
+                        raise Exception(f"Failed to download {repo} after {max_retries} attempts")
+                    
+            if not check_files_exist():
+                raise Exception(f"Failed to download required files for {repo}")
+    
+    @staticmethod
+    def _setup_environment(attn_backend: str, sparse_backend: str, spconv_algo: str, smooth_k: bool):
+        try:
+            success = set_attention_backend(attn_backend)
+            if not success:
+                print(f"Warning: Failed to set {attn_backend}, fallback to default")
+
+            success2 = set_sparse_backend(sparse_backend, spconv_algo)
+            if not success2:
+                print(f"Warning: Failed to set {sparse_backend}, fallback to default")
+
+            os.environ['SAGEATTN_SMOOTH_K'] = '1' if smooth_k else '0'
+        except Exception as e:
+            print(f"Warning: Could not setup environment: {e}")
+    
+    @staticmethod
+    def _optimize_trellis_pipeline(pipeline, use_fp16: bool = True):
+        if DEVICE.type == "cuda":
+            try:
+                if hasattr(pipeline, 'cuda'):
+                    pipeline.cuda()
+
+                if use_fp16:
+                    if hasattr(pipeline, 'enable_attention_slicing'):
+                        pipeline.enable_attention_slicing(slice_size="auto")
+                    if hasattr(pipeline, 'half'):
+                        pipeline.half()
+            except Exception as e:
+                print(f"Warning: Some pipeline optimizations failed: {str(e)}")
+
+        return pipeline 
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/stable_trellis_model_manager.py b/Gen_3D_Modules/Stable3DGen/stable_trellis_model_manager.py
new file mode 100644
index 0000000..d1e3498
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/stable_trellis_model_manager.py
@@ -0,0 +1,292 @@
+# trellis_model_manager.py
+import os
+import logging
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from safetensors.torch import load_file
+import folder_paths
+from huggingface_hub import hf_hub_download, snapshot_download
+from typing import Dict, Union
+import json
+import importlib  # Import the importlib module
+from trellis.modules.utils import convert_module_to_f16, convert_module_to_f32
+
+logger = logging.getLogger('model_manager')
+
+__attributes = {
+    'SparseStructureDecoder': 'trellis.models.sparse_structure_vae',
+    'SparseStructureFlowModel': 'trellis.models.sparse_structure_flow',
+    'SLatFlowModel': 'trellis.models.structured_latent_flow',
+}
+
+__all__ = list(__attributes.keys())
+
+def __getattr__(name):
+    if name in __attributes:
+        module_name = __attributes[name]
+        module = importlib.import_module(module_name, package=None) # Import the module
+        return getattr(module, name)
+    raise AttributeError(f"module {__name__} has no attribute {name}")
+
+class TrellisModelManager:
+    """
+    Basic manager for Trellis models, using ComfyUI's new model path.
+    """
+    def __init__(self, model_dir: str, config=None, device: str = "cuda"):
+        """
+        Initialize the model manager with a specific model directory.
+        
+        Args:
+            model_dir (str): Path to model directory (e.g. "models/checkpoints/TRELLIS-image-large")
+            config (dict or object): Global configuration for Trellis
+            device (str): Device to load models on (e.g. "cuda")
+        """
+        self.model_dir = model_dir
+        if config is None:
+            self.device = device
+        elif isinstance(config, dict):
+            self.device = config.get('device', device)
+            self.config = config
+        else:
+            self.device = getattr(config, 'device', device)
+            self.config = config
+        self.model = None
+        self.dinov2_model = None
+        
+    def load(self) -> None:
+        """Load model configuration and checkpoints"""
+        try:
+            os.makedirs(self.model_dir, exist_ok=True)
+            ckpts_folder = os.path.join(self.model_dir, "ckpts")
+            os.makedirs(ckpts_folder, exist_ok=True)
+            
+            if not os.path.exists(os.path.join(self.model_dir, "pipeline.json")):
+                logger.info("Downloading TRELLIS models...")
+                try:
+                    snapshot_download(
+                        repo_id="Stable-X/trellis-normal-v0-1",
+                        local_dir=self.model_dir,
+                        local_dir_use_symlinks=False,
+                        allow_patterns=["pipeline.json", "README.md"]
+                    )
+                    snapshot_download(
+                        repo_id="Stable-X/trellis-normal-v0-1",
+                        local_dir=ckpts_folder,
+                        local_dir_use_symlinks=False,
+                        allow_patterns=["*.safetensors", "*.json"],
+                        cache_dir=os.path.join(self.model_dir, ".cache")
+                    )
+                    logger.info("Model files downloaded successfully")
+                except Exception as e:
+                    logger.error(f"Error downloading model files: {str(e)}")
+                    raise
+            
+            self.config = self._load_config()
+            
+        except Exception as e:
+            logger.error(f"Error in load(): {str(e)}")
+            raise
+
+    def get_checkpoint_path(self, filename: str) -> str:
+        """
+        Returns the full path to a checkpoint file.
+        """
+        ckpts_folder = os.path.join(self.model_dir, "ckpts")
+        if not filename.endswith('.safetensors'):
+            filename = f"{filename}.safetensors"
+        full_path = os.path.join(ckpts_folder, filename)
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"Checkpoint file not found: {full_path}")
+        return full_path
+
+    def _load_config(self) -> Dict:
+        """Load model configuration from pipeline.json"""
+        try:
+            config_path = os.path.join(self.model_dir, "pipeline.json")
+            
+            if os.path.exists(config_path):
+                logger.info(f"Loading config from {config_path}")
+                with open(config_path, 'r') as f:
+                    config = json.load(f)
+            else:
+                logger.info(f"Config not found locally, downloading from HuggingFace")
+                config_path = hf_hub_download(
+                    repo_id=f"JeffreyXiang/{os.path.basename(self.model_dir)}", 
+                    filename="pipeline.json",
+                    cache_dir=os.path.join(self.model_dir, ".cache")
+                )
+                with open(config_path, 'r') as f:
+                    config = json.load(f)
+            
+            logger.info("Raw config contents:")
+            logger.info(json.dumps(config, indent=2))
+            
+            if not config:
+                raise ValueError(f"Could not load valid configuration from {self.model_dir}")
+                
+            if 'name' not in config:
+                config['name'] = 'TrellisImageTo3DPipeline'
+                
+            return config
+            
+        except Exception as e:
+            logger.error(f"Error loading config from {self.model_dir}: {e}")
+            return {
+                'name': 'TrellisImageTo3DPipeline',
+                'version': '1.0'
+            }
+
+    def load_models(self) -> Dict[str, nn.Module]:
+        """Load all required models with current configuration"""
+        return {
+            'sparse_structure_flow_model': self.get_checkpoint_path("ss_flow_img_dit_L_16l8_fp16"),
+            'slat_flow_model': self.get_checkpoint_path("slat_flow_img_dit_L_64l8p2_fp16")
+        }
+
+    def load_model_components(self) -> Dict[str, nn.Module]:
+        """Loads individual model components."""
+        models = {}
+        model_paths = self.load_models()
+        for name, path in model_paths.items():
+            models[name] = models.from_pretrained(path, config=self.config)
+            
+            # Ensure each model is converted to the desired precision
+            if self.config.get('use_fp16', True):
+                convert_module_to_f16(models[name])
+            else:
+                convert_module_to_f32(models[name])
+    
+        # DINOv2 is handled separately
+        # models['image_cond_model'] = self.load_dinov2(self.config.get("dinov2_model", "dinov2_vitl14"))
+        
+        return models
+
+    def load_dinov2(self, model_name: str):
+        """Load DINOv2 model from local repo and weights"""
+        try:
+            local_repo_path = self._ensure_dinov2_repo()
+            weights_path = self._ensure_dinov2_weights(model_name)
+            
+            use_fp16 = (self.config.get('use_fp16', True) 
+                    if isinstance(self.config, dict) 
+                    else getattr(self.config, 'use_fp16', True))
+            
+            model = torch.hub.load(
+                local_repo_path,
+                model_name,
+                source='local',
+                pretrained=False,  
+                force_reload=False,
+                trust_repo=True
+            )
+            
+            state_dict = torch.load(weights_path, map_location='cpu')
+            
+            if 'model' in state_dict:
+                state_dict = state_dict['model']
+            elif 'state_dict' in state_dict:
+                state_dict = state_dict['state_dict']
+            
+            model.load_state_dict(state_dict, strict=False)
+            
+            model = model.to(self.device)
+            if use_fp16:
+                model = model.half()
+            
+            model.eval()
+            logger.info(f"DINOv2 model {model_name} loaded successfully")
+            return model
+                
+        except Exception as e:
+            raise RuntimeError(f"Error loading DINOv2 model: {str(e)}")
+
+    def _ensure_dinov2_repo(self):
+        repo_path = os.path.join(os.path.dirname(__file__), 'dinov2')
+        
+        if not os.path.exists(repo_path):
+            logger.error("DINOv2 repository not found. Please ensure dinov2/ folder exists with hubconf.py")
+            raise FileNotFoundError(f"DINOv2 repo not found at {repo_path}")
+        
+        return repo_path
+
+    def _ensure_dinov2_weights(self, model_name: str):
+        weights_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 
+                                  'Checkpoints', 'facebookresearch', 'dinov2')
+        os.makedirs(weights_dir, exist_ok=True)
+        
+        weights_file = f"{model_name}.pth"
+        weights_path = os.path.join(weights_dir, weights_file)
+        
+        if not os.path.exists(weights_path):
+            logger.info(f"Downloading {model_name} weights...")
+            
+            success = self._download_from_facebook(model_name, weights_path)
+            
+            if not success:
+                logger.info("Trying HuggingFace...")
+                self._download_from_huggingface(model_name, weights_path)
+                logger.info("Weights downloaded via HuggingFace")
+        
+        return weights_path
+    
+    def _download_from_facebook(self, model_name: str, weights_path: str) -> bool:
+        try:
+            import urllib.request
+            import shutil
+            
+            facebook_urls = {
+                'dinov2_vitl14_reg': 'https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_reg4_pretrain.pth'
+            }
+            
+            if model_name not in facebook_urls:
+                return False
+            
+            url = facebook_urls[model_name]
+            logger.info(f"Downloading from Facebook...")
+            
+            temp_path = weights_path + '.tmp'
+            urllib.request.urlretrieve(url, temp_path)
+            
+            shutil.move(temp_path, weights_path)
+            
+            return True
+            
+        except Exception as e:
+            logger.warning(f"Facebook download failed: {str(e)}")
+            temp_path = weights_path + '.tmp'
+            if os.path.exists(temp_path):
+                os.remove(temp_path)
+            return False
+    
+    def _download_from_huggingface(self, model_name: str, weights_path: str):
+        """Download DINOv2 weights from HuggingFace as fallback"""
+        try:
+            weights_dir = os.path.dirname(weights_path)
+            
+            model_mapping = {
+                'dinov2_vitl14_reg': ('DenisKochetov/dinov2_vitl14_reg', 'dinov2_vitl14_reg.pth')
+            }
+            
+            if model_name not in model_mapping:
+                raise ValueError(f"Unknown model: {model_name}. Available: {list(model_mapping.keys())}")
+            
+            repo_id, filename = model_mapping[model_name]
+            
+            downloaded_path = hf_hub_download(
+                repo_id=repo_id,
+                filename=filename,
+                local_dir=weights_dir,
+                local_dir_use_symlinks=False,
+                resume_download=True
+            )
+            
+            weights_file = os.path.basename(weights_path)
+            if filename != weights_file:
+                final_path = weights_path
+                if os.path.exists(downloaded_path) and downloaded_path != final_path:
+                    import shutil
+                    shutil.move(downloaded_path, final_path)
+            
+        except Exception as e:
+            raise RuntimeError(f"Failed to download weights for {model_name}: {str(e)}")
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/stablex/__init__.py b/Gen_3D_Modules/Stable3DGen/stablex/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Gen_3D_Modules/Stable3DGen/stablex/controlnetvae.py b/Gen_3D_Modules/Stable3DGen/stablex/controlnetvae.py
new file mode 100644
index 0000000..137eb10
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/stablex/controlnetvae.py
@@ -0,0 +1,223 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from diffusers.models.controlnet import ControlNetOutput
+from diffusers.models import ControlNetModel
+
+
+class ControlNetVAEModel(ControlNetModel):
+    def forward(
+        self,
+        sample: torch.Tensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        controlnet_cond: torch.Tensor = None,
+        conditioning_scale: float = 1.0,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guess_mode: bool = False,
+        return_dict: bool = True,
+    ) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
+        """
+        The [`ControlNetVAEModel`] forward method.
+
+        Args:
+            sample (`torch.Tensor`):
+                The noisy input tensor.
+            timestep (`Union[torch.Tensor, float, int]`):
+                The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.Tensor`):
+                The encoder hidden states.
+            controlnet_cond (`torch.Tensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
+                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
+                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
+                embeddings.
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            added_cond_kwargs (`dict`):
+                Additional conditions for the Stable Diffusion XL UNet.
+            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
+                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
+            guess_mode (`bool`, defaults to `False`):
+                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
+                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
+            return_dict (`bool`, defaults to `True`):
+                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
+                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
+                returned where the first element is the sample tensor.
+        """
+        # check channel order
+
+
+        channel_order = self.config.controlnet_conditioning_channel_order
+
+        if channel_order == "rgb":
+            # in rgb order by default
+            ...
+        elif channel_order == "bgr":
+            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
+        else:
+            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        if self.config.addition_embed_type is not None:
+            if self.config.addition_embed_type == "text":
+                aug_emb = self.add_embedding(encoder_hidden_states)
+
+            elif self.config.addition_embed_type == "text_time":
+                if "text_embeds" not in added_cond_kwargs:
+                    raise ValueError(
+                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                    )
+                text_embeds = added_cond_kwargs.get("text_embeds")
+                if "time_ids" not in added_cond_kwargs:
+                    raise ValueError(
+                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                    )
+                time_ids = added_cond_kwargs.get("time_ids")
+                time_embeds = self.add_time_proj(time_ids.flatten())
+                time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+                add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+                add_embeds = add_embeds.to(emb.dtype)
+                aug_emb = self.add_embedding(add_embeds)
+
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
+                sample = self.mid_block(
+                    sample,
+                    emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+            else:
+                sample = self.mid_block(sample, emb)
+
+        # 5. Control net blocks
+
+        controlnet_down_block_res_samples = ()
+
+        # NOTE that controlnet downblock is zeroconv, we discard
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+            down_block_res_sample = down_block_res_sample
+            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
+
+        down_block_res_samples = controlnet_down_block_res_samples
+
+        mid_block_res_sample = sample
+
+        # 6. scaling
+        if guess_mode and not self.config.global_pool_conditions:
+            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
+            scales = scales * conditioning_scale
+            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
+            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
+        else:
+            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
+            mid_block_res_sample = mid_block_res_sample * conditioning_scale
+
+        if self.config.global_pool_conditions:
+            down_block_res_samples = [
+                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
+            ]
+            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
+
+        if not return_dict:
+            return (down_block_res_samples, mid_block_res_sample)
+
+        return ControlNetOutput(
+            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
+        )
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/stablex/pipeline_yoso.py b/Gen_3D_Modules/Stable3DGen/stablex/pipeline_yoso.py
new file mode 100644
index 0000000..693a300
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/stablex/pipeline_yoso.py
@@ -0,0 +1,578 @@
+# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# --------------------------------------------------------------------------
+# More information and citation instructions are available on the
+# --------------------------------------------------------------------------
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from torchvision import transforms
+import cv2
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+
+from diffusers.image_processor import PipelineImageInput
+from diffusers.models import (
+    AutoencoderKL,
+    UNet2DConditionModel,
+	ControlNetModel,
+)
+from diffusers.schedulers import (
+	DDIMScheduler
+)
+
+from diffusers.utils import (
+    BaseOutput,
+    logging,
+    replace_example_docstring,
+)
+
+
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.controlnet import StableDiffusionControlNetPipeline
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.marigold.marigold_image_processing import MarigoldImageProcessor
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+
+import pdb
+
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+Examples:
+```py
+>>> import diffusers
+>>> import torch
+
+>>> pipe = diffusers.MarigoldNormalsPipeline.from_pretrained(
+...     "prs-eth/marigold-normals-lcm-v0-1", variant="fp16", torch_dtype=torch.float16
+... ).to("cuda")
+
+>>> image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
+>>> normals = pipe(image)
+
+>>> vis = pipe.image_processor.visualize_normals(normals.prediction)
+>>> vis[0].save("einstein_normals.png")
+```
+"""
+
+
+@dataclass
+class YosoOutput(BaseOutput):
+    """
+    Output class for Marigold monocular normals prediction pipeline.
+
+    Args:
+        prediction (`np.ndarray`, `torch.Tensor`):
+            Predicted normals with values in the range [-1, 1]. The shape is always $numimages \times 3 \times height
+            \times width$, regardless of whether the images were passed as a 4D array or a list.
+        uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
+            Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages
+            \times 1 \times height \times width$.
+        latent (`None`, `torch.Tensor`):
+            Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
+            The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
+    """
+
+    prediction: Union[np.ndarray, torch.Tensor]
+    latent: Union[None, torch.Tensor]
+    gaus_noise: Union[None, torch.Tensor]
+
+
+class YosoPipeline(StableDiffusionControlNetPipeline):
+    """ Pipeline for monocular normals estimation using the Marigold method: https://marigoldmonodepth.github.io.
+    Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        text_encoder ([`~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A `UNet2DConditionModel` to denoise the encoded image latents.
+        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
+            Provides additional conditioning to the `unet` during the denoising process. If you set multiple
+            ControlNets as a list, the outputs from each ControlNet are added together to create one combined
+            additional conditioning.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            about a model's potential harms.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
+    """
+
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
+    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        unet: UNet2DConditionModel,
+        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel]],
+        scheduler: Union[DDIMScheduler] = None,
+        safety_checker: StableDiffusionSafetyChecker = None,
+        text_encoder: CLIPTextModel = None,
+        tokenizer: CLIPTokenizer = None,
+        feature_extractor: CLIPImageProcessor = None,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        requires_safety_checker: bool = False,
+        default_denoising_steps: Optional[int] = 1,
+		default_processing_resolution: Optional[int] = 768,
+        prompt="",
+        empty_text_embedding=None,
+        t_start: Optional[int] = 0,
+    ):
+        super().__init__(
+            vae,
+            text_encoder,
+            tokenizer,
+            unet,
+            controlnet,
+            scheduler,
+            safety_checker,
+            feature_extractor,
+            image_encoder,
+            requires_safety_checker,
+                )
+
+        # TODO yoso ImageProcessor
+        self.image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.control_image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.default_denoising_steps = default_denoising_steps
+        self.default_processing_resolution = default_processing_resolution
+        self.empty_text_embedding = empty_text_embedding
+        self.t_start= t_start # target_out latents
+
+    def check_inputs(
+        self,
+        image: PipelineImageInput,
+        num_inference_steps: int,
+        ensemble_size: int,
+        processing_resolution: int,
+        resample_method_input: str,
+        resample_method_output: str,
+        batch_size: int,
+        ensembling_kwargs: Optional[Dict[str, Any]],
+        latents: Optional[torch.Tensor],
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]],
+        output_type: str,
+        output_uncertainty: bool,
+    ) -> int:
+        if num_inference_steps is None:
+            raise ValueError("`num_inference_steps` is not specified and could not be resolved from the model config.")
+        if num_inference_steps < 1:
+            raise ValueError("`num_inference_steps` must be positive.")
+        if ensemble_size < 1:
+            raise ValueError("`ensemble_size` must be positive.")
+        if ensemble_size == 2:
+            logger.warning(
+                "`ensemble_size` == 2 results are similar to no ensembling (1); "
+                "consider increasing the value to at least 3."
+            )
+        if ensemble_size == 1 and output_uncertainty:
+            raise ValueError(
+                "Computing uncertainty by setting `output_uncertainty=True` also requires setting `ensemble_size` "
+                "greater than 1."
+            )
+        if processing_resolution is None:
+            raise ValueError(
+                "`processing_resolution` is not specified and could not be resolved from the model config."
+            )
+        if processing_resolution < 0:
+            raise ValueError(
+                "`processing_resolution` must be non-negative: 0 for native resolution, or any positive value for "
+                "downsampled processing."
+            )
+        if processing_resolution % self.vae_scale_factor != 0:
+            raise ValueError(f"`processing_resolution` must be a multiple of {self.vae_scale_factor}.")
+        if resample_method_input not in ("nearest", "nearest-exact", "bilinear", "bicubic", "area"):
+            raise ValueError(
+                "`resample_method_input` takes string values compatible with PIL library: "
+                "nearest, nearest-exact, bilinear, bicubic, area."
+            )
+        if resample_method_output not in ("nearest", "nearest-exact", "bilinear", "bicubic", "area"):
+            raise ValueError(
+                "`resample_method_output` takes string values compatible with PIL library: "
+                "nearest, nearest-exact, bilinear, bicubic, area."
+            )
+        if batch_size < 1:
+            raise ValueError("`batch_size` must be positive.")
+        if output_type not in ["pt", "np"]:
+            raise ValueError("`output_type` must be one of `pt` or `np`.")
+        if latents is not None and generator is not None:
+            raise ValueError("`latents` and `generator` cannot be used together.")
+        if ensembling_kwargs is not None:
+            if not isinstance(ensembling_kwargs, dict):
+                raise ValueError("`ensembling_kwargs` must be a dictionary.")
+            if "reduction" in ensembling_kwargs and ensembling_kwargs["reduction"] not in ("closest", "mean"):
+                raise ValueError("`ensembling_kwargs['reduction']` can be either `'closest'` or `'mean'`.")
+
+        # image checks
+        num_images = 0
+        W, H = None, None
+        if not isinstance(image, list):
+            image = [image]
+        for i, img in enumerate(image):
+            if isinstance(img, np.ndarray) or torch.is_tensor(img):
+                if img.ndim not in (2, 3, 4):
+                    raise ValueError(f"`image[{i}]` has unsupported dimensions or shape: {img.shape}.")
+                H_i, W_i = img.shape[-2:]
+                N_i = 1
+                if img.ndim == 4:
+                    N_i = img.shape[0]
+            elif isinstance(img, Image.Image):
+                W_i, H_i = img.size
+                N_i = 1
+            else:
+                raise ValueError(f"Unsupported `image[{i}]` type: {type(img)}.")
+            if W is None:
+                W, H = W_i, H_i
+            elif (W, H) != (W_i, H_i):
+                raise ValueError(
+                    f"Input `image[{i}]` has incompatible dimensions {(W_i, H_i)} with the previous images {(W, H)}"
+                )
+            num_images += N_i
+
+        # latents checks
+        if latents is not None:
+            if not torch.is_tensor(latents):
+                raise ValueError("`latents` must be a torch.Tensor.")
+            if latents.dim() != 4:
+                raise ValueError(f"`latents` has unsupported dimensions or shape: {latents.shape}.")
+
+            if processing_resolution > 0:
+                max_orig = max(H, W)
+                new_H = H * processing_resolution // max_orig
+                new_W = W * processing_resolution // max_orig
+                if new_H == 0 or new_W == 0:
+                    raise ValueError(f"Extreme aspect ratio of the input image: [{W} x {H}]")
+                W, H = new_W, new_H
+            w = (W + self.vae_scale_factor - 1) // self.vae_scale_factor
+            h = (H + self.vae_scale_factor - 1) // self.vae_scale_factor
+            shape_expected = (num_images * ensemble_size, self.vae.config.latent_channels, h, w)
+
+            if latents.shape != shape_expected:
+                raise ValueError(f"`latents` has unexpected shape={latents.shape} expected={shape_expected}.")
+
+        # generator checks
+        if generator is not None:
+            if isinstance(generator, list):
+                if len(generator) != num_images * ensemble_size:
+                    raise ValueError(
+                        "The number of generators must match the total number of ensemble members for all input images."
+                    )
+                if not all(g.device.type == generator[0].device.type for g in generator):
+                    raise ValueError("`generator` device placement is not consistent in the list.")
+            elif not isinstance(generator, torch.Generator):
+                raise ValueError(f"Unsupported generator type: {type(generator)}.")
+
+        return num_images
+
+    def progress_bar(self, iterable=None, total=None, desc=None, leave=True):
+        if not hasattr(self, "_progress_bar_config"):
+            self._progress_bar_config = {}
+        elif not isinstance(self._progress_bar_config, dict):
+            raise ValueError(
+                f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
+            )
+
+        progress_bar_config = dict(**self._progress_bar_config)
+        progress_bar_config["desc"] = progress_bar_config.get("desc", desc)
+        progress_bar_config["leave"] = progress_bar_config.get("leave", leave)
+        if iterable is not None:
+            return tqdm(iterable, **progress_bar_config)
+        elif total is not None:
+            return tqdm(total=total, **progress_bar_config)
+        else:
+            raise ValueError("Either `total` or `iterable` has to be defined.")
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_inference_steps: Optional[int] = None,
+        ensemble_size: int = 1,
+        processing_resolution: Optional[int] = None,
+        match_input_resolution: bool = True,
+        resample_method_input: str = "bilinear",
+        resample_method_output: str = "bilinear",
+        batch_size: int = 1,
+        ensembling_kwargs: Optional[Dict[str, Any]] = None,
+        latents: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        output_type: str = "np",
+        output_uncertainty: bool = False,
+        output_latent: bool = False,
+        skip_preprocess: bool = False,
+        return_dict: bool = True,
+        **kwargs,
+    ):
+        """
+        Function invoked when calling the pipeline.
+
+        Args:
+            image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`),
+                `List[torch.Tensor]`: An input image or images used as an input for the normals estimation task. For
+                arrays and tensors, the expected value range is between `[0, 1]`. Passing a batch of images is possible
+                by providing a four-dimensional array or a tensor. Additionally, a list of images of two- or
+                three-dimensional arrays or tensors can be passed. In the latter case, all list elements must have the
+                same width and height.
+            num_inference_steps (`int`, *optional*, defaults to `None`):
+                Number of denoising diffusion steps during inference. The default value `None` results in automatic
+                selection. The number of steps should be at least 10 with the full Marigold models, and between 1 and 4
+                for Marigold-LCM models.
+            ensemble_size (`int`, defaults to `1`):
+                Number of ensemble predictions. Recommended values are 5 and higher for better precision, or 1 for
+                faster inference.
+            processing_resolution (`int`, *optional*, defaults to `None`):
+                Effective processing resolution. When set to `0`, matches the larger input image dimension. This
+                produces crisper predictions, but may also lead to the overall loss of global context. The default
+                value `None` resolves to the optimal value from the model config.
+            match_input_resolution (`bool`, *optional*, defaults to `True`):
+                When enabled, the output prediction is resized to match the input dimensions. When disabled, the longer
+                side of the output will equal to `processing_resolution`.
+            resample_method_input (`str`, *optional*, defaults to `"bilinear"`):
+                Resampling method used to resize input images to `processing_resolution`. The accepted values are:
+                `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`.
+            resample_method_output (`str`, *optional*, defaults to `"bilinear"`):
+                Resampling method used to resize output predictions to match the input resolution. The accepted values
+                are `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`.
+            batch_size (`int`, *optional*, defaults to `1`):
+                Batch size; only matters when setting `ensemble_size` or passing a tensor of images.
+            ensembling_kwargs (`dict`, *optional*, defaults to `None`)
+                Extra dictionary with arguments for precise ensembling control. The following options are available:
+                - reduction (`str`, *optional*, defaults to `"closest"`): Defines the ensembling function applied in
+                  every pixel location, can be either `"closest"` or `"mean"`.
+            latents (`torch.Tensor`, *optional*, defaults to `None`):
+                Latent noise tensors to replace the random initialization. These can be taken from the previous
+                function call's output.
+            generator (`torch.Generator`, or `List[torch.Generator]`, *optional*, defaults to `None`):
+                Random number generator object to ensure reproducibility.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                Preferred format of the output's `prediction` and the optional `uncertainty` fields. The accepted
+                values are: `"np"` (numpy array) or `"pt"` (torch tensor).
+            output_uncertainty (`bool`, *optional*, defaults to `False`):
+                When enabled, the output's `uncertainty` field contains the predictive uncertainty map, provided that
+                the `ensemble_size` argument is set to a value above 2.
+            output_latent (`bool`, *optional*, defaults to `False`):
+                When enabled, the output's `latent` field contains the latent codes corresponding to the predictions
+                within the ensemble. These codes can be saved, modified, and used for subsequent calls with the
+                `latents` argument.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.marigold.MarigoldDepthOutput`] instead of a plain tuple.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.marigold.MarigoldNormalsOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.marigold.MarigoldNormalsOutput`] is returned, otherwise a
+                `tuple` is returned where the first element is the prediction, the second element is the uncertainty
+                (or `None`), and the third is the latent (or `None`).
+        """
+
+        # 0. Resolving variables.
+        device = self._execution_device
+        dtype = self.dtype
+
+        # Model-specific optimal default values leading to fast and reasonable results.
+        if num_inference_steps is None:
+            num_inference_steps = self.default_denoising_steps
+        if processing_resolution is None:
+            processing_resolution = self.default_processing_resolution
+
+        # 1. Check inputs.
+        num_images = self.check_inputs(
+            image,
+            num_inference_steps,
+            ensemble_size,
+            processing_resolution,
+            resample_method_input,
+            resample_method_output,
+            batch_size,
+            ensembling_kwargs,
+            latents,
+            generator,
+            output_type,
+            output_uncertainty,
+        )
+
+        self.empty_text_embedding = torch.zeros(1, 257, 1024).to(device, dtype)
+
+
+        # 4. Preprocess input images. This function loads input image or images of compatible dimensions `(H, W)`,
+        # optionally downsamples them to the `processing_resolution` `(PH, PW)`, where
+        # `max(PH, PW) == processing_resolution`, and pads the dimensions to `(PPH, PPW)` such that these values are
+        # divisible by the latent space downscaling factor (typically 8 in Stable Diffusion). The default value `None`
+        # of `processing_resolution` resolves to the optimal value from the model config. It is a recommended mode of
+        # operation and leads to the most reasonable results. Using the native image resolution or any other processing
+        # resolution can lead to loss of either fine details or global context in the output predictions.
+        if not skip_preprocess:
+            image, padding, original_resolution = self.image_processor.preprocess(
+                image, processing_resolution, resample_method_input, device, dtype
+            )  # [N,3,PPH,PPW]
+        else:
+            padding = (0, 0)
+            original_resolution = image.shape[2:]
+        # 5. Encode input image into latent space. At this step, each of the `N` input images is represented with `E`
+        # ensemble members. Each ensemble member is an independent diffused prediction, just initialized independently.
+        # Latents of each such predictions across all input images and all ensemble members are represented in the
+        # `pred_latent` variable. The variable `image_latent` is of the same shape: it contains each input image encoded
+        # into latent space and replicated `E` times. The latents can be either generated (see `generator` to ensure
+        # reproducibility), or passed explicitly via the `latents` argument. The latter can be set outside the pipeline
+        # code. For example, in the Marigold-LCM video processing demo, the latents initialization of a frame is taken
+        # as a convex combination of the latents output of the pipeline for the previous frame and a newly-sampled
+        # noise. This behavior can be achieved by setting the `output_latent` argument to `True`. The latent space
+        # dimensions are `(h, w)`. Encoding into latent space happens in batches of size `batch_size`.
+        # Model invocation: self.vae.encoder.
+        image_latent, pred_latent, gaus_noise = self.prepare_latents(
+            image, latents, generator, ensemble_size, batch_size
+        )  # [N*E,4,h,w], [N*E,4,h,w]
+
+        del image
+
+        # 6. obtain control_output
+
+        cond_scale =controlnet_conditioning_scale
+        down_block_res_samples, mid_block_res_sample = self.controlnet(
+            image_latent.detach(),
+            self.t_start,
+            encoder_hidden_states=self.empty_text_embedding,
+            conditioning_scale=cond_scale,
+            guess_mode=False,
+            return_dict=False,
+        )
+
+        # 7. YOSO sampling
+        latent_x_t = self.unet(
+            pred_latent,
+            self.t_start,
+            encoder_hidden_states=self.empty_text_embedding,
+            down_block_additional_residuals=down_block_res_samples,
+            mid_block_additional_residual=mid_block_res_sample,
+            return_dict=False,
+        )[0]
+
+
+        del (
+            pred_latent,
+            image_latent,
+        )
+
+        # decoder
+        prediction = self.decode_prediction(latent_x_t)
+        prediction = self.image_processor.unpad_image(prediction, padding)  # [N*E,3,PH,PW]
+
+        prediction = self.image_processor.resize_antialias(
+            prediction, original_resolution, resample_method_output, is_aa=False
+        )  # [N,3,H,W]
+        prediction = self.normalize_normals(prediction)  # [N,3,H,W]
+
+        if output_type == "np":
+            prediction = self.image_processor.pt_to_numpy(prediction)  # [N,H,W,3]
+
+        # 11. Offload all models
+        self.maybe_free_model_hooks()
+
+        return YosoOutput(
+            prediction=prediction,
+            latent=latent_x_t,
+            gaus_noise=gaus_noise,
+        )
+
+    # Copied from diffusers.pipelines.marigold.pipeline_marigold_depth.MarigoldDepthPipeline.prepare_latents
+    def prepare_latents(
+        self,
+        image: torch.Tensor,
+        latents: Optional[torch.Tensor],
+        generator: Optional[torch.Generator],
+        ensemble_size: int,
+        batch_size: int,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        def retrieve_latents(encoder_output):
+            if hasattr(encoder_output, "latent_dist"):
+                return encoder_output.latent_dist.mode()
+            elif hasattr(encoder_output, "latents"):
+                return encoder_output.latents
+            else:
+                raise AttributeError("Could not access latents of provided encoder_output")
+
+        image_latent = torch.cat(
+            [
+                retrieve_latents(self.vae.encode(image[i : i + batch_size]))
+                for i in range(0, image.shape[0], batch_size)
+            ],
+            dim=0,
+        )  # [N,4,h,w]
+        image_latent = image_latent * self.vae.config.scaling_factor
+        image_latent = image_latent.repeat_interleave(ensemble_size, dim=0)  # [N*E,4,h,w]
+        gaus_noise = torch.randn_like(image_latent) 
+        pred_latent = image_latent
+        return image_latent, pred_latent, gaus_noise
+
+    def decode_prediction(self, pred_latent: torch.Tensor) -> torch.Tensor:
+        if pred_latent.dim() != 4 or pred_latent.shape[1] != self.vae.config.latent_channels:
+            raise ValueError(
+                f"Expecting 4D tensor of shape [B,{self.vae.config.latent_channels},H,W]; got {pred_latent.shape}."
+            )
+
+        prediction = self.vae.decode(pred_latent / self.vae.config.scaling_factor, return_dict=False)[0]  # [B,3,H,W]
+
+        prediction = self.normalize_normals(prediction)  # [B,3,H,W]
+
+        return prediction  # [B,3,H,W]
+
+    @staticmethod
+    def normalize_normals(normals: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
+        if normals.dim() != 4 or normals.shape[1] != 3:
+            raise ValueError(f"Expecting 4D tensor of shape [B,3,H,W]; got {normals.shape}.")
+
+        norm = torch.norm(normals, dim=1, keepdim=True)
+        normals /= norm.clamp(min=eps)
+
+        return normals
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/__init__.py
new file mode 100644
index 0000000..bf6f3b3
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/__init__.py
@@ -0,0 +1,5 @@
+from . import models
+from . import modules
+from . import pipelines
+from . import representations
+from . import utils
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/backend_config.py b/Gen_3D_Modules/Stable3DGen/trellis/backend_config.py
new file mode 100644
index 0000000..bd72c38
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/backend_config.py
@@ -0,0 +1,225 @@
+# trellis/backend_config.py
+from typing import *
+import os
+import logging
+import importlib
+
+# Global variables
+BACKEND = 'spconv'  # Default sparse backend
+DEBUG = False       # Debug mode flag
+ATTN = 'xformers' # Default attention backend
+SPCONV_ALGO = 'implicit_gemm'  # Default algorithm
+
+def get_spconv_algo() -> str:
+    """Get current spconv algorithm."""
+    global SPCONV_ALGO
+    return SPCONV_ALGO
+
+def set_spconv_algo(algo: Literal['implicit_gemm', 'native', 'auto']) -> bool:
+    """Set spconv algorithm with validation."""
+    global SPCONV_ALGO
+    
+    if algo not in ['implicit_gemm', 'native', 'auto']:
+        logger.warning(f"Invalid spconv algorithm: {algo}. Must be 'implicit_gemm', 'native', or 'auto'")
+        return False
+        
+    SPCONV_ALGO = algo
+    os.environ['SPCONV_ALGO'] = algo
+    logger.info(f"Set spconv algorithm to: {algo}")
+    return True
+
+logger = logging.getLogger(__name__)
+
+def _try_import_xformers() -> bool:
+    try:
+        import xformers.ops
+        return True
+    except ImportError:
+        return False
+
+def _try_import_flash_attn() -> bool:
+    try:
+        import flash_attn
+        return True
+    except ImportError:
+        return False
+
+def _try_import_sageattention() -> bool:
+    try:
+        import torch.nn.functional as F
+        from sageattention import sageattn
+        F.scaled_dot_product_attention = sageattn
+        #import sageattention
+        return True
+    except ImportError:
+        return False
+
+def _try_import_spconv() -> bool:
+    try:
+        import spconv
+        return True
+    except ImportError:
+        return False
+
+def _try_import_torchsparse() -> bool:
+    try:
+        import torchsparse
+        return True
+    except ImportError:
+        return False
+
+def get_available_backends() -> Dict[str, bool]:
+    """Return dict of available attention backends and their status"""
+    return {
+        'xformers': _try_import_xformers(),
+        'flash_attn': _try_import_flash_attn(),
+        'sage': _try_import_sageattention(),
+        'naive': True,
+        'sdpa': True  # Always available with PyTorch >= 2.0
+    }
+
+def get_available_sparse_backends() -> Dict[str, bool]:
+    """Return dict of available sparse backends and their status"""
+    return {
+        'spconv': _try_import_spconv(),
+        'torchsparse': _try_import_torchsparse()
+    }
+
+def get_attention_backend() -> str:
+    """Get current attention backend"""
+    global ATTN
+    return ATTN
+
+def get_sparse_backend() -> str:
+    """Get current sparse backend"""
+    global BACKEND
+    return BACKEND
+
+def get_debug_mode() -> bool:
+    """Get current debug mode status"""
+    global DEBUG
+    return DEBUG
+
+def __from_env():
+    """Initialize settings from environment variables"""
+    global BACKEND
+    global DEBUG
+    global ATTN
+    
+    env_sparse_backend = os.environ.get('SPARSE_BACKEND')
+    env_sparse_debug = os.environ.get('SPARSE_DEBUG')
+    env_sparse_attn = os.environ.get('SPARSE_ATTN_BACKEND')
+    
+    if env_sparse_backend is not None and env_sparse_backend in ['spconv', 'torchsparse']:
+        BACKEND = env_sparse_backend
+    if env_sparse_debug is not None:
+        DEBUG = env_sparse_debug == '1'
+    if env_sparse_attn is not None and env_sparse_attn in ['xformers', 'flash_attn', 'sage', 'sdpa', 'naive']:
+        ATTN = env_sparse_attn
+        os.environ['SPARSE_ATTN_BACKEND'] = env_sparse_attn
+        os.environ['ATTN_BACKEND'] = env_sparse_attn
+        
+    logger.info(f"[SPARSE] Backend: {BACKEND}, Attention: {ATTN}")
+
+def set_backend(backend: Literal['spconv', 'torchsparse']) -> bool:
+    """Set sparse backend with validation"""
+    global BACKEND
+    
+    backend = backend.lower().strip()
+    logger.info(f"Setting sparse backend to: {backend}")
+
+    if backend == 'spconv':
+        try:
+            import spconv
+            BACKEND = 'spconv'
+            os.environ['SPARSE_BACKEND'] = 'spconv'
+            return True
+        except ImportError:
+            logger.warning("spconv not available")
+            return False
+            
+    elif backend == 'torchsparse':
+        try:
+            import torchsparse
+            BACKEND = 'torchsparse'
+            os.environ['SPARSE_BACKEND'] = 'torchsparse'
+            return True
+        except ImportError:
+            logger.warning("torchsparse not available")
+            return False
+    
+    return False
+
+def set_sparse_backend(backend: Literal['spconv', 'torchsparse'], algo: str = None) -> bool:
+    """Alias for set_backend for backwards compatibility
+    
+    Parameters:
+        backend: The sparse backend to use
+        algo: The algorithm to use (only relevant for spconv backend)
+    """
+    # Call set_backend first
+    result = set_backend(backend)
+    
+    # If algorithm is provided and backend was set successfully
+    if algo is not None and result:
+        set_spconv_algo(algo)
+        
+    return result
+
+def set_debug(debug: bool):
+    """Set debug mode"""
+    global DEBUG
+    DEBUG = debug
+    if debug:
+        os.environ['SPARSE_DEBUG'] = '1'
+    else:
+        os.environ['SPARSE_DEBUG'] = '0'
+
+def set_attn(attn: Literal['xformers', 'flash_attn', 'sage', 'sdpa', 'naive']) -> bool:
+    """Set attention backend with validation"""
+    global ATTN
+    
+    attn = attn.lower().strip()
+    logger.info(f"Setting attention backend to: {attn}")
+
+    if attn == 'xformers' and _try_import_xformers():
+        ATTN = 'xformers'
+        os.environ['SPARSE_ATTN_BACKEND'] = 'xformers'
+        os.environ['ATTN_BACKEND'] = 'xformers'
+        return True
+        
+    elif attn == 'flash_attn' and _try_import_flash_attn():
+        ATTN = 'flash_attn'
+        os.environ['SPARSE_ATTN_BACKEND'] = 'flash_attn'
+        os.environ['ATTN_BACKEND'] = 'flash_attn'
+        return True
+        
+    elif attn == 'sage' and _try_import_sageattention():
+        ATTN = 'sage'
+        os.environ['SPARSE_ATTN_BACKEND'] = 'sage'
+        os.environ['ATTN_BACKEND'] = 'sage'
+        return True
+        
+    elif attn == 'sdpa':
+        ATTN = 'sdpa'
+        os.environ['SPARSE_ATTN_BACKEND'] = 'sdpa'
+        os.environ['ATTN_BACKEND'] = 'sdpa'
+        return True
+    
+    elif attn == 'naive':
+        ATTN = 'naive'
+        os.environ['SPARSE_ATTN_BACKEND'] = 'naive'
+        os.environ['ATTN_BACKEND'] = 'naive'
+        return True
+        
+
+    logger.warning(f"Attention backend {attn} not available")
+    return False
+
+# Add alias for backwards compatibility 
+def set_attention_backend(backend: Literal['xformers', 'flash_attn', 'sage', 'sdpa']) -> bool:
+    """Alias for set_attn for backwards compatibility"""
+    return set_attn(backend)
+
+# Initialize from environment variables on module import
+__from_env()
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/models/__init__.py
new file mode 100644
index 0000000..543247b
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/__init__.py
@@ -0,0 +1,112 @@
+import importlib
+import torch
+import torch.nn as nn
+
+__attributes = {
+    'SparseStructureEncoder': 'sparse_structure_vae',
+    'SparseStructureDecoder': 'sparse_structure_vae',
+    'SparseStructureFlowModel': 'sparse_structure_flow',
+    'SLatEncoder': 'structured_latent_vae',
+    'SLatGaussianDecoder': 'structured_latent_vae',
+    'SLatRadianceFieldDecoder': 'structured_latent_vae',
+    'SLatMeshDecoder': 'structured_latent_vae',
+    'SLatFlowModel': 'structured_latent_flow',
+}
+
+__submodules = []
+
+__all__ = list(__attributes.keys()) + __submodules
+
+def __getattr__(name):
+    if name not in globals():
+        if name in __attributes:
+            module_name = __attributes[name]
+            module = importlib.import_module(f".{module_name}", __name__)
+            globals()[name] = getattr(module, name)
+        elif name in __submodules:
+            module = importlib.import_module(f".{name}", __name__)
+            globals()[name] = module
+        else:
+            raise AttributeError(f"module {__name__} has no attribute {name}")
+    return globals()[name]
+
+
+def from_pretrained(path: str) -> nn.Module:
+    """
+    Load a pretrained model.
+    
+    Args:
+        path (str): Full path to model file or HuggingFace repo ID with model name
+    """
+    import os
+    import json
+    from safetensors.torch import load_file
+
+    # Split path into directory and model name
+    path = os.path.normpath(path)
+    model_dir = os.path.dirname(os.path.dirname(path))  # Go up two levels (past ckpts/)
+    model_name = os.path.basename(path)
+
+    is_local = os.path.exists(model_dir)
+
+    if is_local:
+        # For local paths
+        print(f"Loading local model: {model_name}")
+        model_name = model_name.replace('ckpts/', '').replace('ckpts\\', '')
+        config_path = os.path.normpath(os.path.join(model_dir, "ckpts", f"{model_name}.json"))
+        weights_path = os.path.normpath(os.path.join(model_dir, "ckpts", f"{model_name}.safetensors"))
+        
+        if not os.path.exists(config_path):
+            raise FileNotFoundError(f"Config file not found: {config_path}")
+        if not os.path.exists(weights_path):
+            raise FileNotFoundError(f"Weights file not found: {weights_path}")
+            
+        # Load config
+        with open(config_path, 'r') as f:
+            config = json.load(f)
+            
+        # Create model
+        model = create_model_from_config(config)
+        
+        # Load weights
+        state_dict = load_file(weights_path)
+        model.load_state_dict(state_dict)
+        
+    else:
+        # For HuggingFace paths
+        from huggingface_hub import hf_hub_download
+        
+        config_file = hf_hub_download(path, f"{model_name}.json")
+        with open(config_file, 'r') as f:
+            config = json.load(f)
+            
+        model = create_model_from_config(config)
+        
+        weights_file = hf_hub_download(path, f"{model_name}.safetensors")
+        state_dict = load_file(weights_file)
+        model.load_state_dict(state_dict)
+    
+    return model
+
+def create_model_from_config(config):
+    """Helper function to create model from config"""
+    #print(f"Creating model from config: {config}")
+    model_type = config.get('type') or config.get('name')
+    #print(f"Model type: {model_type}")
+    #print(f"Available model types: {list(__attributes.keys())}")
+    if not model_type in __attributes:
+        raise ValueError(f"Unknown model type: {model_type}")
+    
+    model_class = __getattr__(model_type)
+    #print(f"Model class: {model_class}")
+    args = config.get('args', {})
+    #print(f"Model args: {args}")
+    return model_class(**args)
+
+
+# For Pylance
+if __name__ == '__main__':
+    from .sparse_structure_vae import SparseStructureEncoder, SparseStructureDecoder
+    from .sparse_structure_flow import SparseStructureFlowModel
+    from .structured_latent_vae import SLatEncoder, SLatGaussianDecoder, SLatRadianceFieldDecoder, SLatMeshDecoder
+    from .structured_latent_flow import SLatFlowModel
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/sparse_structure_flow.py b/Gen_3D_Modules/Stable3DGen/trellis/models/sparse_structure_flow.py
new file mode 100644
index 0000000..aee71a9
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/sparse_structure_flow.py
@@ -0,0 +1,200 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from ..modules.utils import convert_module_to_f16, convert_module_to_f32
+from ..modules.transformer import AbsolutePositionEmbedder, ModulatedTransformerCrossBlock
+from ..modules.spatial import patchify, unpatchify
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+
+        Args:
+            t: a 1-D Tensor of N indices, one per batch element.
+                These may be fractional.
+            dim: the dimension of the output.
+            max_period: controls the minimum frequency of the embeddings.
+
+        Returns:
+            an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class SparseStructureFlowModel(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        model_channels: int,
+        cond_channels: int,
+        out_channels: int,
+        num_blocks: int,
+        num_heads: Optional[int] = None,
+        num_head_channels: Optional[int] = 64,
+        mlp_ratio: float = 4,
+        patch_size: int = 2,
+        pe_mode: Literal["ape", "rope"] = "ape",
+        use_fp16: bool = False,
+        use_checkpoint: bool = False,
+        share_mod: bool = False,
+        qk_rms_norm: bool = False,
+        qk_rms_norm_cross: bool = False,
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.cond_channels = cond_channels
+        self.out_channels = out_channels
+        self.num_blocks = num_blocks
+        self.num_heads = num_heads or model_channels // num_head_channels
+        self.mlp_ratio = mlp_ratio
+        self.patch_size = patch_size
+        self.pe_mode = pe_mode
+        self.use_fp16 = use_fp16
+        self.use_checkpoint = use_checkpoint
+        self.share_mod = share_mod
+        self.qk_rms_norm = qk_rms_norm
+        self.qk_rms_norm_cross = qk_rms_norm_cross
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+
+        self.t_embedder = TimestepEmbedder(model_channels)
+        if share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(model_channels, 6 * model_channels, bias=True)
+            )
+
+        if pe_mode == "ape":
+            pos_embedder = AbsolutePositionEmbedder(model_channels, 3)
+            coords = torch.meshgrid(*[torch.arange(res, device=self.device) for res in [resolution // patch_size] * 3], indexing='ij')
+            coords = torch.stack(coords, dim=-1).reshape(-1, 3)
+            pos_emb = pos_embedder(coords)
+            self.register_buffer("pos_emb", pos_emb)
+
+        self.input_layer = nn.Linear(in_channels * patch_size**3, model_channels)
+            
+        self.blocks = nn.ModuleList([
+            ModulatedTransformerCrossBlock(
+                model_channels,
+                cond_channels,
+                num_heads=self.num_heads,
+                mlp_ratio=self.mlp_ratio,
+                attn_mode='full',
+                use_checkpoint=self.use_checkpoint,
+                use_rope=(pe_mode == "rope"),
+                share_mod=share_mod,
+                qk_rms_norm=self.qk_rms_norm,
+                qk_rms_norm_cross=self.qk_rms_norm_cross,
+            )
+            for _ in range(num_blocks)
+        ])
+
+        self.out_layer = nn.Linear(model_channels, out_channels * patch_size**3)
+
+        self.initialize_weights()
+        if use_fp16:
+            self.convert_to_fp16()
+
+    @property
+    def device(self) -> torch.device:
+        """
+        Return the device of the model.
+        """
+        return next(self.parameters()).device
+
+    def convert_to_fp16(self) -> None:
+        """
+        Convert the torso of the model to float16.
+        """
+        self.blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self) -> None:
+        """
+        Convert the torso of the model to float32.
+        """
+        self.blocks.apply(convert_module_to_f32)
+
+    def initialize_weights(self) -> None:
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        if self.share_mod:
+            nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
+        else:
+            for block in self.blocks:
+                nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+                nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.out_layer.weight, 0)
+        nn.init.constant_(self.out_layer.bias, 0)
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
+        assert [*x.shape] == [x.shape[0], self.in_channels, *[self.resolution] * 3], \
+                f"Input shape mismatch, got {x.shape}, expected {[x.shape[0], self.in_channels, *[self.resolution] * 3]}"
+
+        h = patchify(x, self.patch_size)
+        h = h.view(*h.shape[:2], -1).permute(0, 2, 1).contiguous()
+
+        h = self.input_layer(h)
+        h = h + self.pos_emb[None]
+        t_emb = self.t_embedder(t)
+        if self.share_mod:
+            t_emb = self.adaLN_modulation(t_emb)
+        t_emb = t_emb.type(self.dtype)
+        h = h.type(self.dtype)
+        cond = cond.type(self.dtype)
+        for block in self.blocks:
+            h = block(h, t_emb, cond)
+        h = h.type(x.dtype)
+        h = F.layer_norm(h, h.shape[-1:])
+        h = self.out_layer(h)
+
+        h = h.permute(0, 2, 1).view(h.shape[0], h.shape[2], *[self.resolution // self.patch_size] * 3)
+        h = unpatchify(h, self.patch_size).contiguous()
+
+        return h
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/sparse_structure_vae.py b/Gen_3D_Modules/Stable3DGen/trellis/models/sparse_structure_vae.py
new file mode 100644
index 0000000..c3e0913
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/sparse_structure_vae.py
@@ -0,0 +1,306 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ..modules.norm import GroupNorm32, ChannelLayerNorm32
+from ..modules.spatial import pixel_shuffle_3d
+from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32
+
+
+def norm_layer(norm_type: str, *args, **kwargs) -> nn.Module:
+    """
+    Return a normalization layer.
+    """
+    if norm_type == "group":
+        return GroupNorm32(32, *args, **kwargs)
+    elif norm_type == "layer":
+        return ChannelLayerNorm32(*args, **kwargs)
+    else:
+        raise ValueError(f"Invalid norm type {norm_type}")
+
+
+class ResBlock3d(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        out_channels: Optional[int] = None,
+        norm_type: Literal["group", "layer"] = "layer",
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+
+        self.norm1 = norm_layer(norm_type, channels)
+        self.norm2 = norm_layer(norm_type, self.out_channels)
+        self.conv1 = nn.Conv3d(channels, self.out_channels, 3, padding=1)
+        self.conv2 = zero_module(nn.Conv3d(self.out_channels, self.out_channels, 3, padding=1))
+        self.skip_connection = nn.Conv3d(channels, self.out_channels, 1) if channels != self.out_channels else nn.Identity()
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.norm1(x)
+        h = F.silu(h)
+        h = self.conv1(h)
+        h = self.norm2(h)
+        h = F.silu(h)
+        h = self.conv2(h)
+        h = h + self.skip_connection(x)
+        return h
+
+
+class DownsampleBlock3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        mode: Literal["conv", "avgpool"] = "conv",
+    ):
+        assert mode in ["conv", "avgpool"], f"Invalid mode {mode}"
+
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        if mode == "conv":
+            self.conv = nn.Conv3d(in_channels, out_channels, 2, stride=2)
+        elif mode == "avgpool":
+            assert in_channels == out_channels, "Pooling mode requires in_channels to be equal to out_channels"
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "conv"):
+            return self.conv(x)
+        else:
+            return F.avg_pool3d(x, 2)
+
+
+class UpsampleBlock3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        mode: Literal["conv", "nearest"] = "conv",
+    ):
+        assert mode in ["conv", "nearest"], f"Invalid mode {mode}"
+
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        if mode == "conv":
+            self.conv = nn.Conv3d(in_channels, out_channels*8, 3, padding=1)
+        elif mode == "nearest":
+            assert in_channels == out_channels, "Nearest mode requires in_channels to be equal to out_channels"
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "conv"):
+            x = self.conv(x)
+            return pixel_shuffle_3d(x, 2)
+        else:
+            return F.interpolate(x, scale_factor=2, mode="nearest")
+        
+
+class SparseStructureEncoder(nn.Module):
+    """
+    Encoder for Sparse Structure (\mathcal{E}_S in the paper Sec. 3.3).
+    
+    Args:
+        in_channels (int): Channels of the input.
+        latent_channels (int): Channels of the latent representation.
+        num_res_blocks (int): Number of residual blocks at each resolution.
+        channels (List[int]): Channels of the encoder blocks.
+        num_res_blocks_middle (int): Number of residual blocks in the middle.
+        norm_type (Literal["group", "layer"]): Type of normalization layer.
+        use_fp16 (bool): Whether to use FP16.
+    """
+    def __init__(
+        self,
+        in_channels: int,
+        latent_channels: int,
+        num_res_blocks: int,
+        channels: List[int],
+        num_res_blocks_middle: int = 2,
+        norm_type: Literal["group", "layer"] = "layer",
+        use_fp16: bool = False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.latent_channels = latent_channels
+        self.num_res_blocks = num_res_blocks
+        self.channels = channels
+        self.num_res_blocks_middle = num_res_blocks_middle
+        self.norm_type = norm_type
+        self.use_fp16 = use_fp16
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+
+        self.input_layer = nn.Conv3d(in_channels, channels[0], 3, padding=1)
+
+        self.blocks = nn.ModuleList([])
+        for i, ch in enumerate(channels):
+            self.blocks.extend([
+                ResBlock3d(ch, ch)
+                for _ in range(num_res_blocks)
+            ])
+            if i < len(channels) - 1:
+                self.blocks.append(
+                    DownsampleBlock3d(ch, channels[i+1])
+                )
+        
+        self.middle_block = nn.Sequential(*[
+            ResBlock3d(channels[-1], channels[-1])
+            for _ in range(num_res_blocks_middle)
+        ])
+
+        self.out_layer = nn.Sequential(
+            norm_layer(norm_type, channels[-1]),
+            nn.SiLU(),
+            nn.Conv3d(channels[-1], latent_channels*2, 3, padding=1)
+        )
+
+        if use_fp16:
+            self.convert_to_fp16()
+
+    @property
+    def device(self) -> torch.device:
+        """
+        Return the device of the model.
+        """
+        return next(self.parameters()).device
+
+    def convert_to_fp16(self) -> None:
+        """
+        Convert the torso of the model to float16.
+        """
+        self.use_fp16 = True
+        self.dtype = torch.float16
+        self.blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self) -> None:
+        """
+        Convert the torso of the model to float32.
+        """
+        self.use_fp16 = False
+        self.dtype = torch.float32
+        self.blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+
+    def forward(self, x: torch.Tensor, sample_posterior: bool = False, return_raw: bool = False) -> torch.Tensor:
+        h = self.input_layer(x)
+        h = h.type(self.dtype)
+
+        for block in self.blocks:
+            h = block(h)
+        h = self.middle_block(h)
+
+        h = h.type(x.dtype)
+        h = self.out_layer(h)
+
+        mean, logvar = h.chunk(2, dim=1)
+
+        if sample_posterior:
+            std = torch.exp(0.5 * logvar)
+            z = mean + std * torch.randn_like(std)
+        else:
+            z = mean
+            
+        if return_raw:
+            return z, mean, logvar
+        return z
+        
+
+class SparseStructureDecoder(nn.Module):
+    """
+    Decoder for Sparse Structure (\mathcal{D}_S in the paper Sec. 3.3).
+    
+    Args:
+        out_channels (int): Channels of the output.
+        latent_channels (int): Channels of the latent representation.
+        num_res_blocks (int): Number of residual blocks at each resolution.
+        channels (List[int]): Channels of the decoder blocks.
+        num_res_blocks_middle (int): Number of residual blocks in the middle.
+        norm_type (Literal["group", "layer"]): Type of normalization layer.
+        use_fp16 (bool): Whether to use FP16.
+    """ 
+    def __init__(
+        self,
+        out_channels: int,
+        latent_channels: int,
+        num_res_blocks: int,
+        channels: List[int],
+        num_res_blocks_middle: int = 2,
+        norm_type: Literal["group", "layer"] = "layer",
+        use_fp16: bool = False,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.latent_channels = latent_channels
+        self.num_res_blocks = num_res_blocks
+        self.channels = channels
+        self.num_res_blocks_middle = num_res_blocks_middle
+        self.norm_type = norm_type
+        self.use_fp16 = use_fp16
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+
+        self.input_layer = nn.Conv3d(latent_channels, channels[0], 3, padding=1)
+
+        self.middle_block = nn.Sequential(*[
+            ResBlock3d(channels[0], channels[0])
+            for _ in range(num_res_blocks_middle)
+        ])
+
+        self.blocks = nn.ModuleList([])
+        for i, ch in enumerate(channels):
+            self.blocks.extend([
+                ResBlock3d(ch, ch)
+                for _ in range(num_res_blocks)
+            ])
+            if i < len(channels) - 1:
+                self.blocks.append(
+                    UpsampleBlock3d(ch, channels[i+1])
+                )
+
+        self.out_layer = nn.Sequential(
+            norm_layer(norm_type, channels[-1]),
+            nn.SiLU(),
+            nn.Conv3d(channels[-1], out_channels, 3, padding=1)
+        )
+
+        if use_fp16:
+            self.convert_to_fp16()
+
+    @property
+    def device(self) -> torch.device:
+        """
+        Return the device of the model.
+        """
+        return next(self.parameters()).device
+    
+    def convert_to_fp16(self) -> None:
+        """
+        Convert the torso of the model to float16.
+        """
+        self.use_fp16 = True
+        self.dtype = torch.float16
+        self.blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self) -> None:
+        """
+        Convert the torso of the model to float32.
+        """
+        self.use_fp16 = False
+        self.dtype = torch.float32
+        self.blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.input_layer(x)
+        
+        h = h.type(self.dtype)
+                
+        h = self.middle_block(h)
+        for block in self.blocks:
+            h = block(h)
+
+        h = h.type(x.dtype)
+        h = self.out_layer(h)
+        return h
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_flow.py b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_flow.py
new file mode 100644
index 0000000..f1463d7
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_flow.py
@@ -0,0 +1,262 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32
+from ..modules.transformer import AbsolutePositionEmbedder
+from ..modules.norm import LayerNorm32
+from ..modules import sparse as sp
+from ..modules.sparse.transformer import ModulatedSparseTransformerCrossBlock
+from .sparse_structure_flow import TimestepEmbedder
+
+
+class SparseResBlock3d(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        emb_channels: int,
+        out_channels: Optional[int] = None,
+        downsample: bool = False,
+        upsample: bool = False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.out_channels = out_channels or channels
+        self.downsample = downsample
+        self.upsample = upsample
+        
+        assert not (downsample and upsample), "Cannot downsample and upsample at the same time"
+
+        self.norm1 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
+        self.norm2 = LayerNorm32(self.out_channels, elementwise_affine=False, eps=1e-6)
+        self.conv1 = sp.SparseConv3d(channels, self.out_channels, 3)
+        self.conv2 = zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3))
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(emb_channels, 2 * self.out_channels, bias=True),
+        )
+        self.skip_connection = sp.SparseLinear(channels, self.out_channels) if channels != self.out_channels else nn.Identity()
+        self.updown = None
+        if self.downsample:
+            self.updown = sp.SparseDownsample(2)
+        elif self.upsample:
+            self.updown = sp.SparseUpsample(2)
+
+    def _updown(self, x: sp.SparseTensor) -> sp.SparseTensor:
+        if self.updown is not None:
+            x = self.updown(x)
+        return x
+
+    def forward(self, x: sp.SparseTensor, emb: torch.Tensor) -> sp.SparseTensor:
+        emb_out = self.emb_layers(emb).type(x.dtype)
+        scale, shift = torch.chunk(emb_out, 2, dim=1)
+
+        x = self._updown(x)
+        h = x.replace(self.norm1(x.feats))
+        h = h.replace(F.silu(h.feats))
+        h = self.conv1(h)
+        h = h.replace(self.norm2(h.feats)) * (1 + scale) + shift
+        h = h.replace(F.silu(h.feats))
+        h = self.conv2(h)
+        h = h + self.skip_connection(x)
+
+        return h
+    
+
+class SLatFlowModel(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        model_channels: int,
+        cond_channels: int,
+        out_channels: int,
+        num_blocks: int,
+        num_heads: Optional[int] = None,
+        num_head_channels: Optional[int] = 64,
+        mlp_ratio: float = 4,
+        patch_size: int = 2,
+        num_io_res_blocks: int = 2,
+        io_block_channels: List[int] = None,
+        pe_mode: Literal["ape", "rope"] = "ape",
+        use_fp16: bool = False,
+        use_checkpoint: bool = False,
+        use_skip_connection: bool = True,
+        share_mod: bool = False,
+        qk_rms_norm: bool = False,
+        qk_rms_norm_cross: bool = False,
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.cond_channels = cond_channels
+        self.out_channels = out_channels
+        self.num_blocks = num_blocks
+        self.num_heads = num_heads or model_channels // num_head_channels
+        self.mlp_ratio = mlp_ratio
+        self.patch_size = patch_size
+        self.num_io_res_blocks = num_io_res_blocks
+        self.io_block_channels = io_block_channels
+        self.pe_mode = pe_mode
+        self.use_fp16 = use_fp16
+        self.use_checkpoint = use_checkpoint
+        self.use_skip_connection = use_skip_connection
+        self.share_mod = share_mod
+        self.qk_rms_norm = qk_rms_norm
+        self.qk_rms_norm_cross = qk_rms_norm_cross
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+
+        assert int(np.log2(patch_size)) == np.log2(patch_size), "Patch size must be a power of 2"
+        assert np.log2(patch_size) == len(io_block_channels), "Number of IO ResBlocks must match the number of stages"
+
+        self.t_embedder = TimestepEmbedder(model_channels)
+        if share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(model_channels, 6 * model_channels, bias=True)
+            )
+
+        if pe_mode == "ape":
+            self.pos_embedder = AbsolutePositionEmbedder(model_channels)
+
+        self.input_layer = sp.SparseLinear(in_channels, io_block_channels[0])
+        self.input_blocks = nn.ModuleList([])
+        for chs, next_chs in zip(io_block_channels, io_block_channels[1:] + [model_channels]):
+            self.input_blocks.extend([
+                SparseResBlock3d(
+                    chs,
+                    model_channels,
+                    out_channels=chs,
+                )
+                for _ in range(num_io_res_blocks-1)
+            ])
+            self.input_blocks.append(
+                SparseResBlock3d(
+                    chs,
+                    model_channels,
+                    out_channels=next_chs,
+                    downsample=True,
+                )
+            )
+            
+        self.blocks = nn.ModuleList([
+            ModulatedSparseTransformerCrossBlock(
+                model_channels,
+                cond_channels,
+                num_heads=self.num_heads,
+                mlp_ratio=self.mlp_ratio,
+                attn_mode='full',
+                use_checkpoint=self.use_checkpoint,
+                use_rope=(pe_mode == "rope"),
+                share_mod=self.share_mod,
+                qk_rms_norm=self.qk_rms_norm,
+                qk_rms_norm_cross=self.qk_rms_norm_cross,
+            )
+            for _ in range(num_blocks)
+        ])
+
+        self.out_blocks = nn.ModuleList([])
+        for chs, prev_chs in zip(reversed(io_block_channels), [model_channels] + list(reversed(io_block_channels[1:]))):
+            self.out_blocks.append(
+                SparseResBlock3d(
+                    prev_chs * 2 if self.use_skip_connection else prev_chs,
+                    model_channels,
+                    out_channels=chs,
+                    upsample=True,
+                )
+            )
+            self.out_blocks.extend([
+                SparseResBlock3d(
+                    chs * 2 if self.use_skip_connection else chs,
+                    model_channels,
+                    out_channels=chs,
+                )
+                for _ in range(num_io_res_blocks-1)
+            ])
+        self.out_layer = sp.SparseLinear(io_block_channels[0], out_channels)
+
+        self.initialize_weights()
+        if use_fp16:
+            self.convert_to_fp16()
+
+    @property
+    def device(self) -> torch.device:
+        """
+        Return the device of the model.
+        """
+        return next(self.parameters()).device
+
+    def convert_to_fp16(self) -> None:
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.blocks.apply(convert_module_to_f16)
+        self.out_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self) -> None:
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.blocks.apply(convert_module_to_f32)
+        self.out_blocks.apply(convert_module_to_f32)
+
+    def initialize_weights(self) -> None:
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        if self.share_mod:
+            nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
+        else:
+            for block in self.blocks:
+                nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+                nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.out_layer.weight, 0)
+        nn.init.constant_(self.out_layer.bias, 0)
+
+    def forward(self, x: sp.SparseTensor, t: torch.Tensor, cond: torch.Tensor) -> sp.SparseTensor:
+        h = self.input_layer(x).type(self.dtype)
+        t_emb = self.t_embedder(t)
+        if self.share_mod:
+            t_emb = self.adaLN_modulation(t_emb)
+        t_emb = t_emb.type(self.dtype)
+        cond = cond.type(self.dtype)
+
+        skips = []
+        # pack with input blocks
+        for block in self.input_blocks:
+            h = block(h, t_emb)
+            skips.append(h.feats)
+        
+        if self.pe_mode == "ape":
+            h = h + self.pos_embedder(h.coords[:, 1:]).type(self.dtype)
+        for block in self.blocks:
+            h = block(h, t_emb, cond)
+
+        # unpack with output blocks
+        for block, skip in zip(self.out_blocks, reversed(skips)):
+            if self.use_skip_connection:
+                h = block(h.replace(torch.cat([h.feats, skip], dim=1)), t_emb)
+            else:
+                h = block(h, t_emb)
+
+        h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
+        h = self.out_layer(h.type(x.dtype))
+        return h
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/__init__.py
new file mode 100644
index 0000000..7ee517c
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/__init__.py
@@ -0,0 +1,2 @@
+from .encoder import SLatEncoder
+from .decoder_mesh import SLatMeshDecoder
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/base.py b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/base.py
new file mode 100644
index 0000000..ab0bf6a
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/base.py
@@ -0,0 +1,117 @@
+from typing import *
+import torch
+import torch.nn as nn
+from ...modules.utils import convert_module_to_f16, convert_module_to_f32
+from ...modules import sparse as sp
+from ...modules.transformer import AbsolutePositionEmbedder
+from ...modules.sparse.transformer import SparseTransformerBlock
+
+
+def block_attn_config(self):
+    """
+    Return the attention configuration of the model.
+    """
+    for i in range(self.num_blocks):
+        if self.attn_mode == "shift_window":
+            yield "serialized", self.window_size, 0, (16 * (i % 2),) * 3, sp.SerializeMode.Z_ORDER
+        elif self.attn_mode == "shift_sequence":
+            yield "serialized", self.window_size, self.window_size // 2 * (i % 2), (0, 0, 0), sp.SerializeMode.Z_ORDER
+        elif self.attn_mode == "shift_order":
+            yield "serialized", self.window_size, 0, (0, 0, 0), sp.SerializeModes[i % 4]
+        elif self.attn_mode == "full":
+            yield "full", None, None, None, None
+        elif self.attn_mode == "swin":
+            yield "windowed", self.window_size, None, self.window_size // 2 * (i % 2), None
+
+
+class SparseTransformerBase(nn.Module):
+    """
+    Sparse Transformer without output layers.
+    Serve as the base class for encoder and decoder.
+    """
+    def __init__(
+        self,
+        in_channels: int,
+        model_channels: int,
+        num_blocks: int,
+        num_heads: Optional[int] = None,
+        num_head_channels: Optional[int] = 64,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full",
+        window_size: Optional[int] = None,
+        pe_mode: Literal["ape", "rope"] = "ape",
+        use_fp16: bool = False,
+        use_checkpoint: bool = False,
+        qk_rms_norm: bool = False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.num_blocks = num_blocks
+        self.window_size = window_size
+        self.num_heads = num_heads or model_channels // num_head_channels
+        self.mlp_ratio = mlp_ratio
+        self.attn_mode = attn_mode
+        self.pe_mode = pe_mode
+        self.use_fp16 = use_fp16
+        self.use_checkpoint = use_checkpoint
+        self.qk_rms_norm = qk_rms_norm
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+
+        if pe_mode == "ape":
+            self.pos_embedder = AbsolutePositionEmbedder(model_channels)
+
+        self.input_layer = sp.SparseLinear(in_channels, model_channels)
+        self.blocks = nn.ModuleList([
+            SparseTransformerBlock(
+                model_channels,
+                num_heads=self.num_heads,
+                mlp_ratio=self.mlp_ratio,
+                attn_mode=attn_mode,
+                window_size=window_size,
+                shift_sequence=shift_sequence,
+                shift_window=shift_window,
+                serialize_mode=serialize_mode,
+                use_checkpoint=self.use_checkpoint,
+                use_rope=(pe_mode == "rope"),
+                qk_rms_norm=self.qk_rms_norm,
+            )
+            for attn_mode, window_size, shift_sequence, shift_window, serialize_mode in block_attn_config(self)
+        ])
+
+    @property
+    def device(self) -> torch.device:
+        """
+        Return the device of the model.
+        """
+        return next(self.parameters()).device
+
+    def convert_to_fp16(self) -> None:
+        """
+        Convert the torso of the model to float16.
+        """
+        self.blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self) -> None:
+        """
+        Convert the torso of the model to float32.
+        """
+        self.blocks.apply(convert_module_to_f32)
+
+    def initialize_weights(self) -> None:
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+
+    def forward(self, x: sp.SparseTensor) -> sp.SparseTensor:
+        h = self.input_layer(x)
+        if self.pe_mode == "ape":
+            h = h + self.pos_embedder(x.coords[:, 1:])
+        h = h.type(self.dtype)
+        for block in self.blocks:
+            h = block(h)
+        return h
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/decoder_mesh.py b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/decoder_mesh.py
new file mode 100644
index 0000000..75c1b1e
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/decoder_mesh.py
@@ -0,0 +1,167 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from ...modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32
+from ...modules import sparse as sp
+from .base import SparseTransformerBase
+from ...representations import MeshExtractResult
+from ...representations.mesh import SparseFeatures2Mesh
+
+
+class SparseSubdivideBlock3d(nn.Module):
+    """
+    A 3D subdivide block that can subdivide the sparse tensor.
+
+    Args:
+        channels: channels in the inputs and outputs.
+        out_channels: if specified, the number of output channels.
+        num_groups: the number of groups for the group norm.
+    """
+    def __init__(
+        self,
+        channels: int,
+        resolution: int,
+        out_channels: Optional[int] = None,
+        num_groups: int = 32
+    ):
+        super().__init__()
+        self.channels = channels
+        self.resolution = resolution
+        self.out_resolution = resolution * 2
+        self.out_channels = out_channels or channels
+
+        self.act_layers = nn.Sequential(
+            sp.SparseGroupNorm32(num_groups, channels),
+            sp.SparseSiLU()
+        )
+        
+        self.sub = sp.SparseSubdivide()
+        
+        self.out_layers = nn.Sequential(
+            sp.SparseConv3d(channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}"),
+            sp.SparseGroupNorm32(num_groups, self.out_channels),
+            sp.SparseSiLU(),
+            zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3, indice_key=f"res_{self.out_resolution}")),
+        )
+        
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        else:
+            self.skip_connection = sp.SparseConv3d(channels, self.out_channels, 1, indice_key=f"res_{self.out_resolution}")
+        
+    def forward(self, x: sp.SparseTensor) -> sp.SparseTensor:
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        Args:
+            x: an [N x C x ...] Tensor of features.
+        Returns:
+            an [N x C x ...] Tensor of outputs.
+        """
+        h = self.act_layers(x)
+        h = self.sub(h)
+        x = self.sub(x)
+        h = self.out_layers(h)
+        h = h + self.skip_connection(x)
+        return h
+
+
+class SLatMeshDecoder(SparseTransformerBase):
+    def __init__(
+        self,
+        resolution: int,
+        model_channels: int,
+        latent_channels: int,
+        num_blocks: int,
+        num_heads: Optional[int] = None,
+        num_head_channels: Optional[int] = 64,
+        mlp_ratio: float = 4,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin",
+        window_size: int = 8,
+        pe_mode: Literal["ape", "rope"] = "ape",
+        use_fp16: bool = False,
+        use_checkpoint: bool = False,
+        qk_rms_norm: bool = False,
+        representation_config: dict = None,
+    ):
+        super().__init__(
+            in_channels=latent_channels,
+            model_channels=model_channels,
+            num_blocks=num_blocks,
+            num_heads=num_heads,
+            num_head_channels=num_head_channels,
+            mlp_ratio=mlp_ratio,
+            attn_mode=attn_mode,
+            window_size=window_size,
+            pe_mode=pe_mode,
+            use_fp16=use_fp16,
+            use_checkpoint=use_checkpoint,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.resolution = resolution
+        self.rep_config = representation_config
+        self.mesh_extractor = SparseFeatures2Mesh(res=self.resolution*4, use_color=self.rep_config.get('use_color', False))
+        self.out_channels = self.mesh_extractor.feats_channels
+        self.upsample = nn.ModuleList([
+            SparseSubdivideBlock3d(
+                channels=model_channels,
+                resolution=resolution,
+                out_channels=model_channels // 4
+            ),
+            SparseSubdivideBlock3d(
+                channels=model_channels // 4,
+                resolution=resolution * 2,
+                out_channels=model_channels // 8
+            )
+        ])
+        self.out_layer = sp.SparseLinear(model_channels // 8, self.out_channels)
+
+        self.initialize_weights()
+        if use_fp16:
+            self.convert_to_fp16()
+
+    def initialize_weights(self) -> None:
+        super().initialize_weights()
+        # Zero-out output layers:
+        nn.init.constant_(self.out_layer.weight, 0)
+        nn.init.constant_(self.out_layer.bias, 0)
+
+    def convert_to_fp16(self) -> None:
+        """
+        Convert the torso of the model to float16.
+        """
+        super().convert_to_fp16()
+        self.upsample.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self) -> None:
+        """
+        Convert the torso of the model to float32.
+        """
+        super().convert_to_fp32()
+        self.upsample.apply(convert_module_to_f32)  
+    
+    def to_representation(self, x: sp.SparseTensor) -> List[MeshExtractResult]:
+        """
+        Convert a batch of network outputs to 3D representations.
+
+        Args:
+            x: The [N x * x C] sparse tensor output by the network.
+
+        Returns:
+            list of representations
+        """
+        ret = []
+        for i in range(x.shape[0]):
+            mesh = self.mesh_extractor(x[i], training=self.training)
+            ret.append(mesh)
+        return ret
+
+    def forward(self, x: sp.SparseTensor) -> List[MeshExtractResult]:
+        h = super().forward(x)
+        for block in self.upsample:
+            h = block(h)
+        h = h.type(x.dtype)
+        h = self.out_layer(h)
+        return self.to_representation(h)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/encoder.py b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/encoder.py
new file mode 100644
index 0000000..8370921
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/models/structured_latent_vae/encoder.py
@@ -0,0 +1,72 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ...modules import sparse as sp
+from .base import SparseTransformerBase
+
+
+class SLatEncoder(SparseTransformerBase):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        model_channels: int,
+        latent_channels: int,
+        num_blocks: int,
+        num_heads: Optional[int] = None,
+        num_head_channels: Optional[int] = 64,
+        mlp_ratio: float = 4,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin",
+        window_size: int = 8,
+        pe_mode: Literal["ape", "rope"] = "ape",
+        use_fp16: bool = False,
+        use_checkpoint: bool = False,
+        qk_rms_norm: bool = False,
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            model_channels=model_channels,
+            num_blocks=num_blocks,
+            num_heads=num_heads,
+            num_head_channels=num_head_channels,
+            mlp_ratio=mlp_ratio,
+            attn_mode=attn_mode,
+            window_size=window_size,
+            pe_mode=pe_mode,
+            use_fp16=use_fp16,
+            use_checkpoint=use_checkpoint,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.resolution = resolution
+        self.out_layer = sp.SparseLinear(model_channels, 2 * latent_channels)
+
+        self.initialize_weights()
+        if use_fp16:
+            self.convert_to_fp16()
+
+    def initialize_weights(self) -> None:
+        super().initialize_weights()
+        # Zero-out output layers:
+        nn.init.constant_(self.out_layer.weight, 0)
+        nn.init.constant_(self.out_layer.bias, 0)
+
+    def forward(self, x: sp.SparseTensor, sample_posterior=True, return_raw=False):
+        h = super().forward(x)
+        h = h.type(x.dtype)
+        h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
+        h = self.out_layer(h)
+        
+        # Sample from the posterior distribution
+        mean, logvar = h.feats.chunk(2, dim=-1)
+        if sample_posterior:
+            std = torch.exp(0.5 * logvar)
+            z = mean + std * torch.randn_like(std)
+        else:
+            z = mean
+        z = h.replace(z)
+            
+        if return_raw:
+            return z, mean, logvar
+        else:
+            return z
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/__init__.py
new file mode 100644
index 0000000..7f1977a
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/__init__.py
@@ -0,0 +1,16 @@
+#from .attention_utils import enable_sage_attention, disable_sage_attention
+from .attention import (
+    scaled_dot_product_attention,
+    BACKEND,
+    DEBUG,
+    MultiHeadAttention,
+    RotaryPositionEmbedder
+)
+
+__all__ = [
+    'scaled_dot_product_attention',
+    'BACKEND',
+    'DEBUG',
+    'MultiHeadAttention',
+    'RotaryPositionEmbedder'
+]
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/__init__.py
new file mode 100644
index 0000000..0f8f390
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/__init__.py
@@ -0,0 +1,38 @@
+import os
+import logging
+from typing import Literal
+from trellis.backend_config import (
+    get_attention_backend,
+    get_debug_mode,
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+#ATTN = get_attention_backend()
+BACKEND = get_attention_backend()
+DEBUG = get_debug_mode()
+
+def __from_env():
+    """Read current backend configuration"""
+    #global ATTN
+    global BACKEND
+    global DEBUG
+    
+    # Get current settings from central config
+    #ATTN = 
+    BACKEND = get_attention_backend()
+    DEBUG = get_debug_mode()
+    
+    print(f"[ATTENTION] Using backend: {BACKEND}")
+
+from .modules import MultiHeadAttention, RotaryPositionEmbedder
+from .full_attn import scaled_dot_product_attention
+
+__all__ = [
+    'scaled_dot_product_attention',
+    'BACKEND',
+    'DEBUG',
+    'MultiHeadAttention',
+    'RotaryPositionEmbedder'
+]
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/full_attn.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/full_attn.py
new file mode 100644
index 0000000..4547236
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/full_attn.py
@@ -0,0 +1,158 @@
+from typing import *
+import torch
+import math
+from trellis.backend_config import (
+    get_attention_backend,
+    get_debug_mode,
+    get_available_backends
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Get configuration from central config
+BACKEND = get_attention_backend()
+DEBUG = get_debug_mode()
+
+# Get available backends and import if active
+available_backends = get_available_backends()
+
+if BACKEND == "xformers" and available_backends['xformers']:
+    import xformers.ops as xops
+elif BACKEND == "flash_attn" and available_backends['flash_attn']:
+    import flash_attn
+elif BACKEND == "sage" and available_backends['sage']:
+    import torch.nn.functional as F
+    from sageattention import sageattn
+    F.scaled_dot_product_attention = sageattn
+elif BACKEND == "sdpa":
+    from torch.nn.functional import scaled_dot_product_attention as sdpa
+elif BACKEND == "naive":
+    from torch.nn.functional import scaled_dot_product_attention as naive
+else:
+    raise ValueError(f"Unknown attention module: {BACKEND}")
+
+
+__all__ = [
+    'scaled_dot_product_attention',
+]
+
+
+def _naive_sdpa(q, k, v):
+    """
+    Naive implementation of scaled dot product attention.
+    """
+    q = q.permute(0, 2, 1, 3)   # [N, H, L, C]
+    k = k.permute(0, 2, 1, 3)   # [N, H, L, C]
+    v = v.permute(0, 2, 1, 3)   # [N, H, L, C]
+    scale_factor = 1 / math.sqrt(q.size(-1))
+    attn_weight = q @ k.transpose(-2, -1) * scale_factor
+    attn_weight = torch.softmax(attn_weight, dim=-1)
+    out = attn_weight @ v
+    out = out.permute(0, 2, 1, 3)   # [N, L, H, C]
+    return out
+
+
+@overload
+def scaled_dot_product_attention(qkv: torch.Tensor) -> torch.Tensor:
+    """
+    Apply scaled dot product attention.
+
+    Args:
+        qkv (torch.Tensor): A [N, L, 3, H, C] tensor containing Qs, Ks, and Vs.
+    """
+    ...
+
+@overload
+def scaled_dot_product_attention(q: torch.Tensor, kv: torch.Tensor) -> torch.Tensor:
+    """
+    Apply scaled dot product attention.
+
+    Args:
+        q (torch.Tensor): A [N, L, H, C] tensor containing Qs.
+        kv (torch.Tensor): A [N, L, 2, H, C] tensor containing Ks and Vs.
+    """
+    ...
+
+@overload
+def scaled_dot_product_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+    """
+    Apply scaled dot product attention.
+
+    Args:
+        q (torch.Tensor): A [N, L, H, Ci] tensor containing Qs.
+        k (torch.Tensor): A [N, L, H, Ci] tensor containing Ks.
+        v (torch.Tensor): A [N, L, H, Co] tensor containing Vs.
+
+    Note:
+        k and v are assumed to have the same coordinate map.
+    """
+    ...
+
+def scaled_dot_product_attention(*args, **kwargs):
+    arg_names_dict = {
+        1: ['qkv'],
+        2: ['q', 'kv'],
+        3: ['q', 'k', 'v']
+    }
+    num_all_args = len(args) + len(kwargs)
+    assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3"
+    for key in arg_names_dict[num_all_args][len(args):]:
+        assert key in kwargs, f"Missing argument {key}"
+
+    if num_all_args == 1:
+        qkv = args[0] if len(args) > 0 else kwargs['qkv']
+        assert len(qkv.shape) == 5 and qkv.shape[2] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, L, 3, H, C]"
+        device = qkv.device
+
+    elif num_all_args == 2:
+        q = args[0] if len(args) > 0 else kwargs['q']
+        kv = args[1] if len(args) > 1 else kwargs['kv']
+        assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}"
+        assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]"
+        assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]"
+        device = q.device
+
+    elif num_all_args == 3:
+        q = args[0] if len(args) > 0 else kwargs['q']
+        k = args[1] if len(args) > 1 else kwargs['k']
+        v = args[2] if len(args) > 2 else kwargs['v']
+        assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}"
+        assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]"
+        assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]"
+        assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]"
+        device = q.device    
+
+    if BACKEND == 'xformers':
+        if num_all_args == 1:
+            q, k, v = qkv.unbind(dim=2)
+        elif num_all_args == 2:
+            k, v = kv.unbind(dim=2)
+        out = xops.memory_efficient_attention(q, k, v)
+    elif BACKEND == 'flash_attn':
+        if num_all_args == 1:
+            out = flash_attn.flash_attn_qkvpacked_func(qkv)
+        elif num_all_args == 2:
+            out = flash_attn.flash_attn_kvpacked_func(q, kv)
+        elif num_all_args == 3:
+            out = flash_attn.flash_attn_func(q, k, v)
+    elif BACKEND == 'sdpa':
+        if num_all_args == 1:
+            q, k, v = qkv.unbind(dim=2)
+        elif num_all_args == 2:
+            k, v = kv.unbind(dim=2)
+        q = q.permute(0, 2, 1, 3)   # [N, H, L, C]
+        k = k.permute(0, 2, 1, 3)   # [N, H, L, C]
+        v = v.permute(0, 2, 1, 3)   # [N, H, L, C]
+        out = sdpa(q, k, v)         # [N, H, L, C]
+        out = out.permute(0, 2, 1, 3)   # [N, L, H, C]
+    elif BACKEND == 'naive':
+        if num_all_args == 1:
+            q, k, v = qkv.unbind(dim=2)
+        elif num_all_args == 2:
+            k, v = kv.unbind(dim=2)
+        out = _naive_sdpa(q, k, v)
+    else:
+        raise ValueError(f"Unknown attention module: {BACKEND}")
+    
+    return out
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/modules.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/modules.py
new file mode 100644
index 0000000..dbe6235
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention/modules.py
@@ -0,0 +1,146 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .full_attn import scaled_dot_product_attention
+
+
+class MultiHeadRMSNorm(nn.Module):
+    def __init__(self, dim: int, heads: int):
+        super().__init__()
+        self.scale = dim ** 0.5
+        self.gamma = nn.Parameter(torch.ones(heads, dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return (F.normalize(x.float(), dim = -1) * self.gamma * self.scale).to(x.dtype)
+
+
+class RotaryPositionEmbedder(nn.Module):
+    def __init__(self, hidden_size: int, in_channels: int = 3):
+        super().__init__()
+        assert hidden_size % 2 == 0, "Hidden size must be divisible by 2"
+        self.hidden_size = hidden_size
+        self.in_channels = in_channels
+        self.freq_dim = hidden_size // in_channels // 2
+        self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim
+        self.freqs = 1.0 / (10000 ** self.freqs)
+        
+    def _get_phases(self, indices: torch.Tensor) -> torch.Tensor:
+        self.freqs = self.freqs.to(indices.device)
+        phases = torch.outer(indices, self.freqs)
+        phases = torch.polar(torch.ones_like(phases), phases)
+        return phases
+        
+    def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor:
+        x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+        x_rotated = x_complex * phases
+        x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype)
+        return x_embed
+        
+    def forward(self, q: torch.Tensor, k: torch.Tensor, indices: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+            q (sp.SparseTensor): [..., N, D] tensor of queries
+            k (sp.SparseTensor): [..., N, D] tensor of keys
+            indices (torch.Tensor): [..., N, C] tensor of spatial positions
+        """
+        if indices is None:
+            indices = torch.arange(q.shape[-2], device=q.device)
+            if len(q.shape) > 2:
+                indices = indices.unsqueeze(0).expand(q.shape[:-2] + (-1,))
+        
+        phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1)
+        if phases.shape[1] < self.hidden_size // 2:
+            phases = torch.cat([phases, torch.polar(
+                torch.ones(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device),
+                torch.zeros(*phases.shape[:-1], self.hidden_size // 2 - phases.shape[1], device=phases.device)
+            )], dim=-1)
+        q_embed = self._rotary_embedding(q, phases)
+        k_embed = self._rotary_embedding(k, phases)
+        return q_embed, k_embed
+    
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int,
+        ctx_channels: Optional[int]=None,
+        type: Literal["self", "cross"] = "self",
+        attn_mode: Literal["full", "windowed"] = "full",
+        window_size: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        qkv_bias: bool = True,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+    ):
+        super().__init__()
+        assert channels % num_heads == 0
+        assert type in ["self", "cross"], f"Invalid attention type: {type}"
+        assert attn_mode in ["full", "windowed"], f"Invalid attention mode: {attn_mode}"
+        assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention"
+        
+        if attn_mode == "windowed":
+            raise NotImplementedError("Windowed attention is not yet implemented")
+        
+        self.channels = channels
+        self.head_dim = channels // num_heads
+        self.ctx_channels = ctx_channels if ctx_channels is not None else channels
+        self.num_heads = num_heads
+        self._type = type
+        self.attn_mode = attn_mode
+        self.window_size = window_size
+        self.shift_window = shift_window
+        self.use_rope = use_rope
+        self.qk_rms_norm = qk_rms_norm
+
+        if self._type == "self":
+            self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias)
+        else:
+            self.to_q = nn.Linear(channels, channels, bias=qkv_bias)
+            self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias)
+            
+        if self.qk_rms_norm:
+            self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads)
+            self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads)
+            
+        self.to_out = nn.Linear(channels, channels)
+
+        if use_rope:
+            self.rope = RotaryPositionEmbedder(channels)
+    
+    def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, indices: Optional[torch.Tensor] = None) -> torch.Tensor:
+        B, L, C = x.shape
+        if self._type == "self":
+            qkv = self.to_qkv(x)
+            qkv = qkv.reshape(B, L, 3, self.num_heads, -1)
+            if self.use_rope:
+                q, k, v = qkv.unbind(dim=2)
+                q, k = self.rope(q, k, indices)
+                qkv = torch.stack([q, k, v], dim=2)
+            if self.attn_mode == "full":
+                if self.qk_rms_norm:
+                    q, k, v = qkv.unbind(dim=2)
+                    q = self.q_rms_norm(q)
+                    k = self.k_rms_norm(k)
+                    h = scaled_dot_product_attention(q, k, v)
+                else:
+                    h = scaled_dot_product_attention(qkv)
+            elif self.attn_mode == "windowed":
+                raise NotImplementedError("Windowed attention is not yet implemented")
+        else:
+            Lkv = context.shape[1]
+            q = self.to_q(x)
+            kv = self.to_kv(context)
+            q = q.reshape(B, L, self.num_heads, -1)
+            kv = kv.reshape(B, Lkv, 2, self.num_heads, -1)
+            if self.qk_rms_norm:
+                q = self.q_rms_norm(q)
+                k, v = kv.unbind(dim=2)
+                k = self.k_rms_norm(k)
+                h = scaled_dot_product_attention(q, k, v)
+            else:
+                h = scaled_dot_product_attention(q, kv)
+        h = h.reshape(B, L, -1)
+        h = self.to_out(h)
+        return h
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/attention_utils.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention_utils.py
new file mode 100644
index 0000000..71cef4e
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/attention_utils.py
@@ -0,0 +1,25 @@
+#sage_attn.py
+import os
+from typing import Optional
+import torch
+import torch.nn.functional as F
+from sageattention import sageattn
+import math
+
+__all__ = ['SageAttention', 'sage_attention']
+
+
+def enable_sage_attention():
+    """
+    Enable SageAttention by replacing PyTorch's scaled_dot_product_attention
+    with sageattn from the SageAttention library.
+    """
+    F.scaled_dot_product_attention = sageattn
+    return True
+
+def disable_sage_attention():
+    """
+    Restore PyTorch's original scaled_dot_product_attention function.
+    """
+    F.scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
+    return True
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/norm.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/norm.py
new file mode 100644
index 0000000..0903572
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/norm.py
@@ -0,0 +1,25 @@
+import torch
+import torch.nn as nn
+
+
+class LayerNorm32(nn.LayerNorm):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return super().forward(x.float()).type(x.dtype)
+    
+
+class GroupNorm32(nn.GroupNorm):
+    """
+    A GroupNorm layer that converts to float32 before the forward pass.
+    """
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return super().forward(x.float()).type(x.dtype)
+    
+    
+class ChannelLayerNorm32(LayerNorm32):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        DIM = x.dim()
+        x = x.permute(0, *range(2, DIM), 1).contiguous()
+        x = super().forward(x)
+        x = x.permute(0, DIM-1, *range(1, DIM-1)).contiguous()
+        return x
+    
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/__init__.py
new file mode 100644
index 0000000..f2602fc
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/__init__.py
@@ -0,0 +1,67 @@
+from typing import *
+import importlib
+from trellis.backend_config import get_sparse_backend, get_attention_backend, get_debug_mode
+
+BACKEND = get_sparse_backend()
+DEBUG = get_debug_mode()
+ATTN = get_attention_backend()
+
+if ATTN not in ['xformers', 'flash_attn']:
+    logger.warning(f"Attention backend {ATTN} not supported for sparse attention. Only 'xformers' and 'flash_attn' are available. Defaulting to 'flash_attn'")
+    ATTN = 'flash_attn'
+
+__attributes = {
+    'SparseTensor': 'basic',
+    'sparse_batch_broadcast': 'basic',
+    'sparse_batch_op': 'basic',
+    'sparse_cat': 'basic',
+    'sparse_unbind': 'basic',
+    'SparseGroupNorm': 'norm',
+    'SparseLayerNorm': 'norm',
+    'SparseGroupNorm32': 'norm',
+    'SparseLayerNorm32': 'norm',
+    'SparseReLU': 'nonlinearity',
+    'SparseSiLU': 'nonlinearity',
+    'SparseGELU': 'nonlinearity',
+    'SparseActivation': 'nonlinearity',
+    'SparseLinear': 'linear',
+    'sparse_scaled_dot_product_attention': 'attention',
+    'SerializeMode': 'attention',
+    'sparse_serialized_scaled_dot_product_self_attention': 'attention',
+    'sparse_windowed_scaled_dot_product_self_attention': 'attention',
+    'SparseMultiHeadAttention': 'attention',
+    'SparseConv3d': 'conv',
+    'SparseInverseConv3d': 'conv',
+    'SparseDownsample': 'spatial',
+    'SparseUpsample': 'spatial',
+    'SparseSubdivide' : 'spatial'
+}
+
+__submodules = ['transformer']
+
+__all__ = list(__attributes.keys()) + __submodules
+
+def __getattr__(name):
+    if name not in globals():
+        if name in __attributes:
+            module_name = __attributes[name]
+            module = importlib.import_module(f".{module_name}", __name__)
+            globals()[name] = getattr(module, name)
+        elif name in __submodules:
+            module = importlib.import_module(f".{name}", __name__)
+            globals()[name] = module
+        else:
+            raise AttributeError(f"module {__name__} has no attribute {name}")
+    return globals()[name]
+
+
+# For Pylance
+if __name__ == '__main__':
+    from .basic import *
+    from .norm import *
+    from .nonlinearity import *
+    from .linear import *
+    from .attention import *
+    from .conv import *
+    from .spatial import *
+    import transformer
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/__init__.py
new file mode 100644
index 0000000..7556277
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/__init__.py
@@ -0,0 +1,31 @@
+from .full_attn import *
+from .serialized_attn import *
+from .windowed_attn import *
+from .modules import *
+import os
+import logging
+from typing import Literal
+from trellis.backend_config import (
+    get_attention_backend,
+    get_debug_mode,
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+#ATTN = get_attention_backend()
+ATTN = get_attention_backend()
+DEBUG = get_debug_mode()
+
+def __from_env():
+    """Read current backend configuration"""
+    #global ATTN
+    global ATTN
+    global DEBUG
+    
+    # Get current settings from central config
+    #ATTN = 
+    ATTN = get_attention_backend()
+    DEBUG = get_debug_mode()
+    
+    print(f"[ATTENTION] sparse backend: {ATTN}")
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/full_attn.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/full_attn.py
new file mode 100644
index 0000000..b80c008
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/full_attn.py
@@ -0,0 +1,236 @@
+#trellis\modules\sparse\attention\full_attn.py
+from typing import *
+import torch
+import math
+from .. import SparseTensor
+from trellis.backend_config import (
+    get_attention_backend,
+    get_debug_mode,
+    get_available_backends
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Get configuration from central config
+ATTN = get_attention_backend()
+DEBUG = get_debug_mode()
+
+# Get available backends and import if active
+available_backends = get_available_backends()
+
+if ATTN not in ['xformers', 'flash_attn']:
+    logger.warning(f"Attention backend {ATTN} not supported for sparse attention. Only 'xformers' and 'flash_attn' are available. Defaulting to 'flash_attn'")
+    ATTN = 'flash_attn'
+
+if ATTN == 'xformers' and available_backends['xformers']:
+    import xformers.ops as xops
+elif ATTN == 'flash_attn' and available_backends['flash_attn']:
+    import flash_attn
+else:
+    raise ImportError(f"Could not import {ATTN}. Please install either xformers or flash-attn for sparse attention support.")
+
+
+
+__all__ = [
+    'sparse_scaled_dot_product_attention',
+]
+
+
+@overload
+def sparse_scaled_dot_product_attention(qkv: SparseTensor) -> SparseTensor:
+    """
+    Apply scaled dot product attention to a sparse tensor.
+
+    Args:
+        qkv (SparseTensor): A [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs.
+    """
+    ...
+
+@overload
+def sparse_scaled_dot_product_attention(q: SparseTensor, kv: Union[SparseTensor, torch.Tensor]) -> SparseTensor:
+    """
+    Apply scaled dot product attention to a sparse tensor.
+
+    Args:
+        q (SparseTensor): A [N, *, H, C] sparse tensor containing Qs.
+        kv (SparseTensor or torch.Tensor): A [N, *, 2, H, C] sparse tensor or a [N, L, 2, H, C] dense tensor containing Ks and Vs.
+    """
+    ...
+
+@overload
+def sparse_scaled_dot_product_attention(q: torch.Tensor, kv: SparseTensor) -> torch.Tensor:
+    """
+    Apply scaled dot product attention to a sparse tensor.
+
+    Args:
+        q (SparseTensor): A [N, L, H, C] dense tensor containing Qs.
+        kv (SparseTensor or torch.Tensor): A [N, *, 2, H, C] sparse tensor containing Ks and Vs.
+    """
+    ...
+
+@overload
+def sparse_scaled_dot_product_attention(q: SparseTensor, k: SparseTensor, v: SparseTensor) -> SparseTensor:
+    """
+    Apply scaled dot product attention to a sparse tensor.
+
+    Args:
+        q (SparseTensor): A [N, *, H, Ci] sparse tensor containing Qs.
+        k (SparseTensor): A [N, *, H, Ci] sparse tensor containing Ks.
+        v (SparseTensor): A [N, *, H, Co] sparse tensor containing Vs.
+
+    Note:
+        k and v are assumed to have the same coordinate map.
+    """
+    ...
+
+@overload
+def sparse_scaled_dot_product_attention(q: SparseTensor, k: torch.Tensor, v: torch.Tensor) -> SparseTensor:
+    """
+    Apply scaled dot product attention to a sparse tensor.
+
+    Args:
+        q (SparseTensor): A [N, *, H, Ci] sparse tensor containing Qs.
+        k (torch.Tensor): A [N, L, H, Ci] dense tensor containing Ks.
+        v (torch.Tensor): A [N, L, H, Co] dense tensor containing Vs.
+    """
+    ...
+
+@overload
+def sparse_scaled_dot_product_attention(q: torch.Tensor, k: SparseTensor, v: SparseTensor) -> torch.Tensor:
+    """
+    Apply scaled dot product attention to a sparse tensor.
+
+    Args:
+        q (torch.Tensor): A [N, L, H, Ci] dense tensor containing Qs.
+        k (SparseTensor): A [N, *, H, Ci] sparse tensor containing Ks.
+        v (SparseTensor): A [N, *, H, Co] sparse tensor containing Vs.
+    """
+    ...
+
+def sparse_scaled_dot_product_attention(*args, **kwargs):
+    arg_names_dict = {
+        1: ['qkv'],
+        2: ['q', 'kv'],
+        3: ['q', 'k', 'v']
+    }
+    num_all_args = len(args) + len(kwargs)
+    assert num_all_args in arg_names_dict, f"Invalid number of arguments, got {num_all_args}, expected 1, 2, or 3"
+    for key in arg_names_dict[num_all_args][len(args):]:
+        assert key in kwargs, f"Missing argument {key}"
+
+    if num_all_args == 1:
+        qkv = args[0] if len(args) > 0 else kwargs['qkv']
+        assert isinstance(qkv, SparseTensor), f"qkv must be a SparseTensor, got {type(qkv)}"
+        assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]"
+        device = qkv.device
+
+        s = qkv
+        q_seqlen = [qkv.layout[i].stop - qkv.layout[i].start for i in range(qkv.shape[0])]
+        kv_seqlen = q_seqlen
+        qkv = qkv.feats     # [T, 3, H, C]
+
+    elif num_all_args == 2:
+        q = args[0] if len(args) > 0 else kwargs['q']
+        kv = args[1] if len(args) > 1 else kwargs['kv']
+        assert isinstance(q, SparseTensor) and isinstance(kv, (SparseTensor, torch.Tensor)) or \
+               isinstance(q, torch.Tensor) and isinstance(kv, SparseTensor), \
+               f"Invalid types, got {type(q)} and {type(kv)}"
+        assert q.shape[0] == kv.shape[0], f"Batch size mismatch, got {q.shape[0]} and {kv.shape[0]}"
+        device = q.device
+
+        if isinstance(q, SparseTensor):
+            assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, C]"
+            s = q
+            q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])]
+            q = q.feats     # [T_Q, H, C]
+        else:
+            assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, C]"
+            s = None
+            N, L, H, C = q.shape
+            q_seqlen = [L] * N
+            q = q.reshape(N * L, H, C)   # [T_Q, H, C]
+
+        if isinstance(kv, SparseTensor):
+            assert len(kv.shape) == 4 and kv.shape[1] == 2, f"Invalid shape for kv, got {kv.shape}, expected [N, *, 2, H, C]"
+            kv_seqlen = [kv.layout[i].stop - kv.layout[i].start for i in range(kv.shape[0])]
+            kv = kv.feats     # [T_KV, 2, H, C]
+        else:
+            assert len(kv.shape) == 5, f"Invalid shape for kv, got {kv.shape}, expected [N, L, 2, H, C]"
+            N, L, _, H, C = kv.shape
+            kv_seqlen = [L] * N
+            kv = kv.reshape(N * L, 2, H, C)   # [T_KV, 2, H, C]
+
+    elif num_all_args == 3:
+        q = args[0] if len(args) > 0 else kwargs['q']
+        k = args[1] if len(args) > 1 else kwargs['k']
+        v = args[2] if len(args) > 2 else kwargs['v']
+        assert isinstance(q, SparseTensor) and isinstance(k, (SparseTensor, torch.Tensor)) and type(k) == type(v) or \
+               isinstance(q, torch.Tensor) and isinstance(k, SparseTensor) and isinstance(v, SparseTensor), \
+               f"Invalid types, got {type(q)}, {type(k)}, and {type(v)}"
+        assert q.shape[0] == k.shape[0] == v.shape[0], f"Batch size mismatch, got {q.shape[0]}, {k.shape[0]}, and {v.shape[0]}"
+        device = q.device
+
+        if isinstance(q, SparseTensor):
+            assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, Ci]"
+            s = q
+            q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])]
+            q = q.feats     # [T_Q, H, Ci]
+        else:
+            assert len(q.shape) == 4, f"Invalid shape for q, got {q.shape}, expected [N, L, H, Ci]"
+            s = None
+            N, L, H, CI = q.shape
+            q_seqlen = [L] * N
+            q = q.reshape(N * L, H, CI)  # [T_Q, H, Ci]
+
+        if isinstance(k, SparseTensor):
+            assert len(k.shape) == 3, f"Invalid shape for k, got {k.shape}, expected [N, *, H, Ci]"
+            assert len(v.shape) == 3, f"Invalid shape for v, got {v.shape}, expected [N, *, H, Co]"
+            kv_seqlen = [k.layout[i].stop - k.layout[i].start for i in range(k.shape[0])]
+            k = k.feats     # [T_KV, H, Ci]
+            v = v.feats     # [T_KV, H, Co]
+        else:
+            assert len(k.shape) == 4, f"Invalid shape for k, got {k.shape}, expected [N, L, H, Ci]"
+            assert len(v.shape) == 4, f"Invalid shape for v, got {v.shape}, expected [N, L, H, Co]"
+            N, L, H, CI, CO = *k.shape, v.shape[-1]
+            kv_seqlen = [L] * N
+            k = k.reshape(N * L, H, CI)     # [T_KV, H, Ci]
+            v = v.reshape(N * L, H, CO)     # [T_KV, H, Co]
+
+    if DEBUG:
+        if s is not None:
+            for i in range(s.shape[0]):
+                assert (s.coords[s.layout[i]] == i).all(), f"SparseScaledDotProductSelfAttention: batch index mismatch"
+        if num_all_args in [2, 3]:
+            assert q.shape[:2] == [1, sum(q_seqlen)], f"SparseScaledDotProductSelfAttention: q shape mismatch"
+        if num_all_args == 3:
+            assert k.shape[:2] == [1, sum(kv_seqlen)], f"SparseScaledDotProductSelfAttention: k shape mismatch"
+            assert v.shape[:2] == [1, sum(kv_seqlen)], f"SparseScaledDotProductSelfAttention: v shape mismatch"
+
+    if ATTN == 'xformers':
+        if num_all_args == 1:
+            q, k, v = qkv.unbind(dim=1)
+        elif num_all_args == 2:
+            k, v = kv.unbind(dim=1)
+        q = q.unsqueeze(0)
+        k = k.unsqueeze(0)
+        v = v.unsqueeze(0)
+        mask = xops.fmha.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen)
+        out = xops.memory_efficient_attention(q, k, v, mask)[0]
+    elif ATTN == 'flash_attn':
+        cu_seqlens_q = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(q_seqlen), dim=0)]).int().to(device)
+        if num_all_args in [2, 3]:
+            cu_seqlens_kv = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(kv_seqlen), dim=0)]).int().to(device)
+        if num_all_args == 1:
+            out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens_q, max(q_seqlen))
+        elif num_all_args == 2:
+            out = flash_attn.flash_attn_varlen_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen))
+        elif num_all_args == 3:
+            out = flash_attn.flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen))
+    else:
+        raise ValueError(f"Unknown attention module: {ATTN}")
+    
+    if s is not None:
+        return s.replace(out)
+    else:
+        return out.reshape(N, L, H, -1)
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/modules.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/modules.py
new file mode 100644
index 0000000..5d2fe78
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/modules.py
@@ -0,0 +1,139 @@
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .. import SparseTensor
+from .full_attn import sparse_scaled_dot_product_attention
+from .serialized_attn import SerializeMode, sparse_serialized_scaled_dot_product_self_attention
+from .windowed_attn import sparse_windowed_scaled_dot_product_self_attention
+from ...attention import RotaryPositionEmbedder
+
+
+class SparseMultiHeadRMSNorm(nn.Module):
+    def __init__(self, dim: int, heads: int):
+        super().__init__()
+        self.scale = dim ** 0.5
+        self.gamma = nn.Parameter(torch.ones(heads, dim))
+
+    def forward(self, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]:
+        x_type = x.dtype
+        x = x.float()
+        if isinstance(x, SparseTensor):
+            x = x.replace(F.normalize(x.feats, dim=-1))
+        else:
+            x = F.normalize(x, dim=-1)            
+        return (x * self.gamma * self.scale).to(x_type)
+
+
+class SparseMultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int,
+        ctx_channels: Optional[int] = None,
+        type: Literal["self", "cross"] = "self",
+        attn_mode: Literal["full", "serialized", "windowed"] = "full",
+        window_size: Optional[int] = None,
+        shift_sequence: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        serialize_mode: Optional[SerializeMode] = None,
+        qkv_bias: bool = True,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+    ):
+        super().__init__()
+        assert channels % num_heads == 0
+        assert type in ["self", "cross"], f"Invalid attention type: {type}"
+        assert attn_mode in ["full", "serialized", "windowed"], f"Invalid attention mode: {attn_mode}"
+        assert type == "self" or attn_mode == "full", "Cross-attention only supports full attention"
+        assert type == "self" or use_rope is False, "Rotary position embeddings only supported for self-attention"
+        self.channels = channels
+        self.ctx_channels = ctx_channels if ctx_channels is not None else channels
+        self.num_heads = num_heads
+        self._type = type
+        self.attn_mode = attn_mode
+        self.window_size = window_size
+        self.shift_sequence = shift_sequence
+        self.shift_window = shift_window
+        self.serialize_mode = serialize_mode
+        self.use_rope = use_rope
+        self.qk_rms_norm = qk_rms_norm
+
+        if self._type == "self":
+            self.to_qkv = nn.Linear(channels, channels * 3, bias=qkv_bias)
+        else:
+            self.to_q = nn.Linear(channels, channels, bias=qkv_bias)
+            self.to_kv = nn.Linear(self.ctx_channels, channels * 2, bias=qkv_bias)
+        
+        if self.qk_rms_norm:
+            self.q_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads)
+            self.k_rms_norm = SparseMultiHeadRMSNorm(channels // num_heads, num_heads)
+            
+        self.to_out = nn.Linear(channels, channels)
+
+        if use_rope:
+            self.rope = RotaryPositionEmbedder(channels)
+
+    @staticmethod
+    def _linear(module: nn.Linear, x: Union[SparseTensor, torch.Tensor]) -> Union[SparseTensor, torch.Tensor]:
+        if isinstance(x, SparseTensor):
+            return x.replace(module(x.feats))
+        else:
+            return module(x)
+
+    @staticmethod
+    def _reshape_chs(x: Union[SparseTensor, torch.Tensor], shape: Tuple[int, ...]) -> Union[SparseTensor, torch.Tensor]:
+        if isinstance(x, SparseTensor):
+            return x.reshape(*shape)
+        else:
+            return x.reshape(*x.shape[:2], *shape)
+
+    def _fused_pre(self, x: Union[SparseTensor, torch.Tensor], num_fused: int) -> Union[SparseTensor, torch.Tensor]:
+        if isinstance(x, SparseTensor):
+            x_feats = x.feats.unsqueeze(0)
+        else:
+            x_feats = x
+        x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1)
+        return x.replace(x_feats.squeeze(0)) if isinstance(x, SparseTensor) else x_feats
+
+    def _rope(self, qkv: SparseTensor) -> SparseTensor:
+        q, k, v = qkv.feats.unbind(dim=1)   # [T, H, C]
+        q, k = self.rope(q, k, qkv.coords[:, 1:])
+        qkv = qkv.replace(torch.stack([q, k, v], dim=1)) 
+        return qkv
+    
+    def forward(self, x: Union[SparseTensor, torch.Tensor], context: Optional[Union[SparseTensor, torch.Tensor]] = None) -> Union[SparseTensor, torch.Tensor]:
+        if self._type == "self":
+            qkv = self._linear(self.to_qkv, x)
+            qkv = self._fused_pre(qkv, num_fused=3)
+            if self.use_rope:
+                qkv = self._rope(qkv)
+            if self.qk_rms_norm:
+                q, k, v = qkv.unbind(dim=1)
+                q = self.q_rms_norm(q)
+                k = self.k_rms_norm(k)
+                qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1))
+            if self.attn_mode == "full":
+                h = sparse_scaled_dot_product_attention(qkv)
+            elif self.attn_mode == "serialized":
+                h = sparse_serialized_scaled_dot_product_self_attention(
+                    qkv, self.window_size, serialize_mode=self.serialize_mode, shift_sequence=self.shift_sequence, shift_window=self.shift_window
+                )
+            elif self.attn_mode == "windowed":
+                h = sparse_windowed_scaled_dot_product_self_attention(
+                    qkv, self.window_size, shift_window=self.shift_window
+                )
+        else:
+            q = self._linear(self.to_q, x)
+            q = self._reshape_chs(q, (self.num_heads, -1))
+            kv = self._linear(self.to_kv, context)
+            kv = self._fused_pre(kv, num_fused=2)
+            if self.qk_rms_norm:
+                q = self.q_rms_norm(q)
+                k, v = kv.unbind(dim=1)
+                k = self.k_rms_norm(k)
+                kv = kv.replace(torch.stack([k.feats, v.feats], dim=1))
+            h = sparse_scaled_dot_product_attention(q, kv)
+        h = self._reshape_chs(h, (-1,))
+        h = self._linear(self.to_out, h)
+        return h
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/serialized_attn.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/serialized_attn.py
new file mode 100644
index 0000000..f62c8cf
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/serialized_attn.py
@@ -0,0 +1,211 @@
+from typing import *
+from enum import Enum
+import torch
+import math
+from .. import SparseTensor
+from trellis.backend_config import (
+    get_attention_backend,
+    get_debug_mode,
+    get_available_backends
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Get configuration from central config
+ATTN = get_attention_backend()
+DEBUG = get_debug_mode()
+
+# Get available backends and import if active
+available_backends = get_available_backends()
+
+if ATTN not in ['xformers', 'flash_attn']:
+    logger.warning(f"Attention backend {ATTN} not supported for sparse attention. Only 'xformers' and 'flash_attn' are available. Defaulting to 'flash_attn'")
+    ATTN = 'flash_attn'
+
+if ATTN == 'xformers' and available_backends['xformers']:
+    import xformers.ops as xops
+elif ATTN == 'flash_attn' and available_backends['flash_attn']:
+    import flash_attn
+else:
+    raise ImportError(f"Could not import {ATTN}. Please install either xformers or flash-attn for sparse attention support.")
+
+
+__all__ = [
+    'sparse_serialized_scaled_dot_product_self_attention',
+]
+
+
+class SerializeMode(Enum):
+    Z_ORDER = 0
+    Z_ORDER_TRANSPOSED = 1
+    HILBERT = 2
+    HILBERT_TRANSPOSED = 3
+
+
+SerializeModes = [
+    SerializeMode.Z_ORDER,
+    SerializeMode.Z_ORDER_TRANSPOSED,
+    SerializeMode.HILBERT,
+    SerializeMode.HILBERT_TRANSPOSED
+]
+
+
+def calc_serialization(
+    tensor: SparseTensor,
+    window_size: int,
+    serialize_mode: SerializeMode = SerializeMode.Z_ORDER,
+    shift_sequence: int = 0,
+    shift_window: Tuple[int, int, int] = (0, 0, 0)
+) -> Tuple[torch.Tensor, torch.Tensor, List[int]]:
+    """
+    Calculate serialization and partitioning for a set of coordinates.
+
+    Args:
+        tensor (SparseTensor): The input tensor.
+        window_size (int): The window size to use.
+        serialize_mode (SerializeMode): The serialization mode to use.
+        shift_sequence (int): The shift of serialized sequence.
+        shift_window (Tuple[int, int, int]): The shift of serialized coordinates.
+
+    Returns:
+        (torch.Tensor, torch.Tensor): Forwards and backwards indices.
+    """
+    fwd_indices = []
+    bwd_indices = []
+    seq_lens = []
+    seq_batch_indices = []
+    offsets = [0]
+    
+    if 'vox2seq' not in globals():
+        import vox2seq
+
+    # Serialize the input
+    serialize_coords = tensor.coords[:, 1:].clone()
+    serialize_coords += torch.tensor(shift_window, dtype=torch.int32, device=tensor.device).reshape(1, 3)
+    if serialize_mode == SerializeMode.Z_ORDER:
+        code = vox2seq.encode(serialize_coords, mode='z_order', permute=[0, 1, 2])
+    elif serialize_mode == SerializeMode.Z_ORDER_TRANSPOSED:
+        code = vox2seq.encode(serialize_coords, mode='z_order', permute=[1, 0, 2])
+    elif serialize_mode == SerializeMode.HILBERT:
+        code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[0, 1, 2])
+    elif serialize_mode == SerializeMode.HILBERT_TRANSPOSED:
+        code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[1, 0, 2])
+    else:
+        raise ValueError(f"Unknown serialize mode: {serialize_mode}")
+    
+    for bi, s in enumerate(tensor.layout):
+        num_points = s.stop - s.start
+        num_windows = (num_points + window_size - 1) // window_size
+        valid_window_size = num_points / num_windows
+        to_ordered = torch.argsort(code[s.start:s.stop])
+        if num_windows == 1:
+            fwd_indices.append(to_ordered)
+            bwd_indices.append(torch.zeros_like(to_ordered).scatter_(0, to_ordered, torch.arange(num_points, device=tensor.device)))
+            fwd_indices[-1] += s.start
+            bwd_indices[-1] += offsets[-1]
+            seq_lens.append(num_points)
+            seq_batch_indices.append(bi)
+            offsets.append(offsets[-1] + seq_lens[-1])
+        else:
+            # Partition the input
+            offset = 0
+            mids = [(i + 0.5) * valid_window_size + shift_sequence for i in range(num_windows)]
+            split = [math.floor(i * valid_window_size + shift_sequence) for i in range(num_windows + 1)]
+            bwd_index = torch.zeros((num_points,), dtype=torch.int64, device=tensor.device)
+            for i in range(num_windows):
+                mid = mids[i]
+                valid_start = split[i]
+                valid_end = split[i + 1]
+                padded_start = math.floor(mid - 0.5 * window_size)
+                padded_end = padded_start + window_size
+                fwd_indices.append(to_ordered[torch.arange(padded_start, padded_end, device=tensor.device) % num_points])
+                offset += valid_start - padded_start
+                bwd_index.scatter_(0, fwd_indices[-1][valid_start-padded_start:valid_end-padded_start], torch.arange(offset, offset + valid_end - valid_start, device=tensor.device))
+                offset += padded_end - valid_start
+                fwd_indices[-1] += s.start
+            seq_lens.extend([window_size] * num_windows)
+            seq_batch_indices.extend([bi] * num_windows)
+            bwd_indices.append(bwd_index + offsets[-1])
+            offsets.append(offsets[-1] + num_windows * window_size)
+
+    fwd_indices = torch.cat(fwd_indices)
+    bwd_indices = torch.cat(bwd_indices)
+
+    return fwd_indices, bwd_indices, seq_lens, seq_batch_indices
+    
+
+def sparse_serialized_scaled_dot_product_self_attention(
+    qkv: SparseTensor,
+    window_size: int,
+    serialize_mode: SerializeMode = SerializeMode.Z_ORDER,
+    shift_sequence: int = 0,
+    shift_window: Tuple[int, int, int] = (0, 0, 0)
+) -> SparseTensor:
+    """
+    Apply serialized scaled dot product self attention to a sparse tensor.
+
+    Args:
+        qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs.
+        window_size (int): The window size to use.
+        serialize_mode (SerializeMode): The serialization mode to use.
+        shift_sequence (int): The shift of serialized sequence.
+        shift_window (Tuple[int, int, int]): The shift of serialized coordinates.
+        shift (int): The shift to use.
+    """
+    assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]"
+
+    serialization_spatial_cache_name = f'serialization_{serialize_mode}_{window_size}_{shift_sequence}_{shift_window}'
+    serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name)
+    if serialization_spatial_cache is None:
+        fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_serialization(qkv, window_size, serialize_mode, shift_sequence, shift_window)
+        qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices))
+    else:
+        fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache
+
+    M = fwd_indices.shape[0]
+    T = qkv.feats.shape[0]
+    H = qkv.feats.shape[2]
+    C = qkv.feats.shape[3]
+    
+    qkv_feats = qkv.feats[fwd_indices]      # [M, 3, H, C]
+
+    if DEBUG:
+        start = 0
+        qkv_coords = qkv.coords[fwd_indices]
+        for i in range(len(seq_lens)):
+            assert (qkv_coords[start:start+seq_lens[i], 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch"
+            start += seq_lens[i]
+
+    if all([seq_len == window_size for seq_len in seq_lens]):
+        B = len(seq_lens)
+        N = window_size
+        qkv_feats = qkv_feats.reshape(B, N, 3, H, C)
+        if ATTN == 'xformers':
+            q, k, v = qkv_feats.unbind(dim=2)                       # [B, N, H, C]
+            out = xops.memory_efficient_attention(q, k, v)          # [B, N, H, C]
+        elif ATTN == 'flash_attn':
+            out = flash_attn.flash_attn_qkvpacked_func(qkv_feats)   # [B, N, H, C]
+        else:
+            raise ValueError(f"Unknown attention module: {ATTN}")
+        out = out.reshape(B * N, H, C)                              # [M, H, C]
+    else:
+        if ATTN == 'xformers':
+            q, k, v = qkv_feats.unbind(dim=1)                       # [M, H, C]
+            q = q.unsqueeze(0)                                      # [1, M, H, C]
+            k = k.unsqueeze(0)                                      # [1, M, H, C]
+            v = v.unsqueeze(0)                                      # [1, M, H, C]
+            mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
+            out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C]
+        elif ATTN == 'flash_attn':
+            cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \
+                        .to(qkv.device).int()
+            out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C]
+
+    out = out[bwd_indices]      # [T, H, C]
+
+    if DEBUG:
+        qkv_coords = qkv_coords[bwd_indices]
+        assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch"
+
+    return qkv.replace(out)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/windowed_attn.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/windowed_attn.py
new file mode 100644
index 0000000..c47fd76
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/attention/windowed_attn.py
@@ -0,0 +1,157 @@
+from typing import *
+from enum import Enum
+import math
+import os
+import logging
+import torch
+from .. import SparseTensor
+from trellis.backend_config import (
+    get_attention_backend,
+    get_debug_mode,
+    get_available_backends
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Get configuration from central config
+# Get configuration from central config
+ATTN = get_attention_backend()
+DEBUG = get_debug_mode()
+
+# Get available backends and import if active
+available_backends = get_available_backends()
+
+if ATTN not in ['xformers', 'flash_attn']:
+    logger.warning(f"Attention backend {ATTN} not supported for sparse attention. Only 'xformers' and 'flash_attn' are available. Defaulting to 'flash_attn'")
+    ATTN = 'flash_attn'
+
+if ATTN == 'xformers' and available_backends['xformers']:
+    import xformers.ops as xops
+elif ATTN == 'flash_attn' and available_backends['flash_attn']:
+    import flash_attn
+else:
+    raise ImportError(f"Could not import {ATTN}. Please install either xformers or flash-attn for sparse attention support.")
+
+
+__all__ = [
+    'sparse_windowed_scaled_dot_product_self_attention',
+]
+
+
+def calc_window_partition(
+    tensor: SparseTensor,
+    window_size: Union[int, Tuple[int, ...]],
+    shift_window: Union[int, Tuple[int, ...]] = 0
+) -> Tuple[torch.Tensor, torch.Tensor, List[int], List[int]]:
+    """
+    Calculate serialization and partitioning for a set of coordinates.
+
+    Args:
+        tensor (SparseTensor): The input tensor.
+        window_size (int): The window size to use.
+        shift_window (Tuple[int, ...]): The shift of serialized coordinates.
+
+    Returns:
+        (torch.Tensor): Forwards indices.
+        (torch.Tensor): Backwards indices.
+        (List[int]): Sequence lengths.
+        (List[int]): Sequence batch indices.
+    """
+    DIM = tensor.coords.shape[1] - 1
+    shift_window = (shift_window,) * DIM if isinstance(shift_window, int) else shift_window
+    window_size = (window_size,) * DIM if isinstance(window_size, int) else window_size
+    shifted_coords = tensor.coords.clone().detach()
+    shifted_coords[:, 1:] += torch.tensor(shift_window, device=tensor.device, dtype=torch.int32).unsqueeze(0)
+
+    MAX_COORDS = shifted_coords[:, 1:].max(dim=0).values.tolist()
+    NUM_WINDOWS = [math.ceil((mc + 1) / ws) for mc, ws in zip(MAX_COORDS, window_size)]
+    OFFSET = torch.cumprod(torch.tensor([1] + NUM_WINDOWS[::-1]), dim=0).tolist()[::-1]
+
+    shifted_coords[:, 1:] //= torch.tensor(window_size, device=tensor.device, dtype=torch.int32).unsqueeze(0)
+    shifted_indices = (shifted_coords * torch.tensor(OFFSET, device=tensor.device, dtype=torch.int32).unsqueeze(0)).sum(dim=1)
+    fwd_indices = torch.argsort(shifted_indices)
+    bwd_indices = torch.empty_like(fwd_indices)
+    bwd_indices[fwd_indices] = torch.arange(fwd_indices.shape[0], device=tensor.device)
+    seq_lens = torch.bincount(shifted_indices)
+    seq_batch_indices = torch.arange(seq_lens.shape[0], device=tensor.device, dtype=torch.int32) // OFFSET[0]
+    mask = seq_lens != 0
+    seq_lens = seq_lens[mask].tolist()
+    seq_batch_indices = seq_batch_indices[mask].tolist()
+
+    return fwd_indices, bwd_indices, seq_lens, seq_batch_indices
+    
+
+def sparse_windowed_scaled_dot_product_self_attention(
+    qkv: SparseTensor,
+    window_size: int,
+    shift_window: Tuple[int, int, int] = (0, 0, 0)
+) -> SparseTensor:
+    """
+    Apply windowed scaled dot product self attention to a sparse tensor.
+
+    Args:
+        qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs.
+        window_size (int): The window size to use.
+        shift_window (Tuple[int, int, int]): The shift of serialized coordinates.
+        shift (int): The shift to use.
+    """
+    assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]"
+
+    serialization_spatial_cache_name = f'window_partition_{window_size}_{shift_window}'
+    serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name)
+    if serialization_spatial_cache is None:
+        fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_window_partition(qkv, window_size, shift_window)
+        qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices))
+    else:
+        fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache
+
+    M = fwd_indices.shape[0]
+    T = qkv.feats.shape[0]
+    H = qkv.feats.shape[2]
+    C = qkv.feats.shape[3]
+    
+    qkv_feats = qkv.feats[fwd_indices]      # [M, 3, H, C]
+
+    if DEBUG:
+        start = 0
+        qkv_coords = qkv.coords[fwd_indices]
+        for i in range(len(seq_lens)):
+            seq_coords = qkv_coords[start:start+seq_lens[i]]
+            assert (seq_coords[:, 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch"
+            assert (seq_coords[:, 1:].max(dim=0).values - seq_coords[:, 1:].min(dim=0).values < window_size).all(), \
+                    f"SparseWindowedScaledDotProductSelfAttention: window size exceeded"
+            start += seq_lens[i]
+
+    if all([seq_len == window_size for seq_len in seq_lens]):
+        B = len(seq_lens)
+        N = window_size
+        qkv_feats = qkv_feats.reshape(B, N, 3, H, C)
+        if ATTN == 'xformers':
+            q, k, v = qkv_feats.unbind(dim=2)                       # [B, N, H, C]
+            out = xops.memory_efficient_attention(q, k, v)          # [B, N, H, C]
+        elif ATTN == 'flash_attn':
+            out = flash_attn.flash_attn_qkvpacked_func(qkv_feats)   # [B, N, H, C]
+        else:
+            raise ValueError(f"Unknown attention module: {ATTN}")
+        out = out.reshape(B * N, H, C)                              # [M, H, C]
+    else:
+        if ATTN == 'xformers':
+            q, k, v = qkv_feats.unbind(dim=1)                       # [M, H, C]
+            q = q.unsqueeze(0)                                      # [1, M, H, C]
+            k = k.unsqueeze(0)                                      # [1, M, H, C]
+            v = v.unsqueeze(0)                                      # [1, M, H, C]
+            mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
+            out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C]
+        elif ATTN == 'flash_attn':
+            cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \
+                        .to(qkv.device).int()
+            out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C]
+
+    out = out[bwd_indices]      # [T, H, C]
+
+    if DEBUG:
+        qkv_coords = qkv_coords[bwd_indices]
+        assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch"
+
+    return qkv.replace(out)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/basic.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/basic.py
new file mode 100644
index 0000000..db0d0a9
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/basic.py
@@ -0,0 +1,466 @@
+from typing import *
+import torch
+import torch.nn as nn
+
+from trellis.backend_config import get_debug_mode, get_spconv_algo, get_sparse_backend
+
+
+SparseTensorData = None # Lazy import
+
+DEBUG = get_debug_mode()
+
+SPCONV_ALGO = get_spconv_algo()
+
+BACKEND = get_sparse_backend()
+__all__ = [
+    'SparseTensor',
+    'sparse_batch_broadcast',
+    'sparse_batch_op',
+    'sparse_cat',
+    'sparse_unbind',
+]
+
+
+class SparseTensor:
+    """
+    Sparse tensor with support for both torchsparse and spconv backends.
+    
+    Parameters:
+    - feats (torch.Tensor): Features of the sparse tensor.
+    - coords (torch.Tensor): Coordinates of the sparse tensor.
+    - shape (torch.Size): Shape of the sparse tensor.
+    - layout (List[slice]): Layout of the sparse tensor for each batch
+    - data (SparseTensorData): Sparse tensor data used for convolusion
+
+    NOTE:
+    - Data corresponding to a same batch should be contiguous.
+    - Coords should be in [0, 1023]
+    """
+    @overload
+    def __init__(self, feats: torch.Tensor, coords: torch.Tensor, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ...
+
+    @overload
+    def __init__(self, data, shape: Optional[torch.Size] = None, layout: Optional[List[slice]] = None, **kwargs): ...
+
+    def __init__(self, *args, **kwargs):
+        # Lazy import of sparse tensor backend
+        global SparseTensorData
+        if SparseTensorData is None:
+            import importlib
+            if BACKEND == 'torchsparse':
+                SparseTensorData = importlib.import_module('torchsparse').SparseTensor
+            elif BACKEND == 'spconv':
+                SparseTensorData = importlib.import_module('spconv.pytorch').SparseConvTensor
+                
+        method_id = 0
+        if len(args) != 0:
+            method_id = 0 if isinstance(args[0], torch.Tensor) else 1
+        else:
+            method_id = 1 if 'data' in kwargs else 0
+
+        if method_id == 0:
+            feats, coords, shape, layout = args + (None,) * (4 - len(args))
+            if 'feats' in kwargs:
+                feats = kwargs['feats']
+                del kwargs['feats']
+            if 'coords' in kwargs:
+                coords = kwargs['coords']
+                del kwargs['coords']
+            if 'shape' in kwargs:
+                shape = kwargs['shape']
+                del kwargs['shape']
+            if 'layout' in kwargs:
+                layout = kwargs['layout']
+                del kwargs['layout']
+
+            if shape is None:
+                shape = self.__cal_shape(feats, coords)
+            if layout is None:
+                layout = self.__cal_layout(coords, shape[0])
+            if BACKEND == 'torchsparse':
+                self.data = SparseTensorData(feats, coords, **kwargs)
+            elif BACKEND == 'spconv':
+                spatial_shape = list(coords.max(0)[0] + 1)[1:]
+                self.data = SparseTensorData(feats.reshape(feats.shape[0], -1), coords, spatial_shape, shape[0], **kwargs)
+                self.data._features = feats
+        elif method_id == 1:
+            data, shape, layout = args + (None,) * (3 - len(args))
+            if 'data' in kwargs:
+                data = kwargs['data']
+                del kwargs['data']
+            if 'shape' in kwargs:
+                shape = kwargs['shape']
+                del kwargs['shape']
+            if 'layout' in kwargs:
+                layout = kwargs['layout']
+                del kwargs['layout']
+
+            self.data = data
+            if shape is None:
+                shape = self.__cal_shape(self.feats, self.coords)
+            if layout is None:
+                layout = self.__cal_layout(self.coords, shape[0])
+
+        self._shape = shape
+        self._layout = layout
+        self._scale = kwargs.get('scale', (1, 1, 1))
+        self._spatial_cache = kwargs.get('spatial_cache', {})
+
+        if DEBUG:
+            try:
+                assert self.feats.shape[0] == self.coords.shape[0], f"Invalid feats shape: {self.feats.shape}, coords shape: {self.coords.shape}"
+                assert self.shape == self.__cal_shape(self.feats, self.coords), f"Invalid shape: {self.shape}"
+                assert self.layout == self.__cal_layout(self.coords, self.shape[0]), f"Invalid layout: {self.layout}"
+                for i in range(self.shape[0]):
+                    assert torch.all(self.coords[self.layout[i], 0] == i), f"The data of batch {i} is not contiguous"
+            except Exception as e:
+                print('Debugging information:')
+                print(f"- Shape: {self.shape}")
+                print(f"- Layout: {self.layout}")
+                print(f"- Scale: {self._scale}")
+                print(f"- Coords: {self.coords}")
+                raise e
+        
+    def __cal_shape(self, feats, coords):
+        shape = []
+        shape.append(coords[:, 0].max().item() + 1)
+        shape.extend([*feats.shape[1:]])
+        return torch.Size(shape)
+    
+    def __cal_layout(self, coords, batch_size):
+        seq_len = torch.bincount(coords[:, 0], minlength=batch_size)
+        offset = torch.cumsum(seq_len, dim=0) 
+        layout = [slice((offset[i] - seq_len[i]).item(), offset[i].item()) for i in range(batch_size)]
+        return layout
+    
+    @property
+    def shape(self) -> torch.Size:
+        return self._shape
+    
+    def dim(self) -> int:
+        return len(self.shape)
+    
+    @property
+    def layout(self) -> List[slice]:
+        return self._layout
+
+    @property
+    def feats(self) -> torch.Tensor:
+        if BACKEND == 'torchsparse':
+            return self.data.F
+        elif BACKEND == 'spconv':
+            return self.data.features
+    
+    @feats.setter
+    def feats(self, value: torch.Tensor):
+        if BACKEND == 'torchsparse':
+            self.data.F = value
+        elif BACKEND == 'spconv':
+            self.data.features = value
+
+    @property
+    def coords(self) -> torch.Tensor:
+        if BACKEND == 'torchsparse':
+            return self.data.C
+        elif BACKEND == 'spconv':
+            return self.data.indices
+        
+    @coords.setter
+    def coords(self, value: torch.Tensor):
+        if BACKEND == 'torchsparse':
+            self.data.C = value
+        elif BACKEND == 'spconv':
+            self.data.indices = value
+
+    @property
+    def dtype(self):
+        return self.feats.dtype
+
+    @property
+    def device(self):
+        return self.feats.device
+
+    @overload
+    def to(self, dtype: torch.dtype) -> 'SparseTensor': ...
+
+    @overload
+    def to(self, device: Optional[Union[str, torch.device]] = None, dtype: Optional[torch.dtype] = None) -> 'SparseTensor': ...
+
+    def to(self, *args, **kwargs) -> 'SparseTensor':
+        device = None
+        dtype = None
+        if len(args) == 2:
+            device, dtype = args
+        elif len(args) == 1:
+            if isinstance(args[0], torch.dtype):
+                dtype = args[0]
+            else:
+                device = args[0]
+        if 'dtype' in kwargs:
+            assert dtype is None, "to() received multiple values for argument 'dtype'"
+            dtype = kwargs['dtype']
+        if 'device' in kwargs:
+            assert device is None, "to() received multiple values for argument 'device'"
+            device = kwargs['device']
+        
+        new_feats = self.feats.to(device=device, dtype=dtype)
+        new_coords = self.coords.to(device=device)
+        return self.replace(new_feats, new_coords)
+
+    def type(self, dtype):
+        new_feats = self.feats.type(dtype)
+        return self.replace(new_feats)
+
+    def cpu(self) -> 'SparseTensor':
+        new_feats = self.feats.cpu()
+        new_coords = self.coords.cpu()
+        return self.replace(new_feats, new_coords)
+    
+    def cuda(self) -> 'SparseTensor':
+        new_feats = self.feats.cuda()
+        new_coords = self.coords.cuda()
+        return self.replace(new_feats, new_coords)
+
+    def half(self) -> 'SparseTensor':
+        new_feats = self.feats.half()
+        return self.replace(new_feats)
+    
+    def float(self) -> 'SparseTensor':
+        new_feats = self.feats.float()
+        return self.replace(new_feats)
+    
+    def detach(self) -> 'SparseTensor':
+        new_coords = self.coords.detach()
+        new_feats = self.feats.detach()
+        return self.replace(new_feats, new_coords)
+
+    def dense(self) -> torch.Tensor:
+        if BACKEND == 'torchsparse':
+            return self.data.dense()
+        elif BACKEND == 'spconv':
+            return self.data.dense()
+
+    def reshape(self, *shape) -> 'SparseTensor':
+        new_feats = self.feats.reshape(self.feats.shape[0], *shape)
+        return self.replace(new_feats)
+    
+    def unbind(self, dim: int) -> List['SparseTensor']:
+        return sparse_unbind(self, dim)
+
+    def replace(self, feats: torch.Tensor, coords: Optional[torch.Tensor] = None) -> 'SparseTensor':
+        new_shape = [self.shape[0]]
+        new_shape.extend(feats.shape[1:])
+        if BACKEND == 'torchsparse':
+            new_data = SparseTensorData(
+                feats=feats,
+                coords=self.data.coords if coords is None else coords,
+                stride=self.data.stride,
+                spatial_range=self.data.spatial_range,
+            )
+            new_data._caches = self.data._caches
+        elif BACKEND == 'spconv':
+            new_data = SparseTensorData(
+                self.data.features.reshape(self.data.features.shape[0], -1),
+                self.data.indices,
+                self.data.spatial_shape,
+                self.data.batch_size,
+                self.data.grid,
+                self.data.voxel_num,
+                self.data.indice_dict
+            )
+            new_data._features = feats
+            new_data.benchmark = self.data.benchmark
+            new_data.benchmark_record = self.data.benchmark_record
+            new_data.thrust_allocator = self.data.thrust_allocator
+            new_data._timer = self.data._timer
+            new_data.force_algo = self.data.force_algo
+            new_data.int8_scale = self.data.int8_scale
+            if coords is not None:
+                new_data.indices = coords
+        new_tensor = SparseTensor(new_data, shape=torch.Size(new_shape), layout=self.layout, scale=self._scale, spatial_cache=self._spatial_cache)
+        return new_tensor
+
+    @staticmethod
+    def full(aabb, dim, value, dtype=torch.float32, device=None) -> 'SparseTensor':
+        N, C = dim
+        x = torch.arange(aabb[0], aabb[3] + 1)
+        y = torch.arange(aabb[1], aabb[4] + 1)
+        z = torch.arange(aabb[2], aabb[5] + 1)
+        coords = torch.stack(torch.meshgrid(x, y, z, indexing='ij'), dim=-1).reshape(-1, 3)
+        coords = torch.cat([
+            torch.arange(N).view(-1, 1).repeat(1, coords.shape[0]).view(-1, 1),
+            coords.repeat(N, 1),
+        ], dim=1).to(dtype=torch.int32, device=device)
+        feats = torch.full((coords.shape[0], C), value, dtype=dtype, device=device)
+        return SparseTensor(feats=feats, coords=coords)
+
+    def __merge_sparse_cache(self, other: 'SparseTensor') -> dict:
+        new_cache = {}
+        for k in set(list(self._spatial_cache.keys()) + list(other._spatial_cache.keys())):
+            if k in self._spatial_cache:
+                new_cache[k] = self._spatial_cache[k]
+            if k in other._spatial_cache:
+                if k not in new_cache:
+                    new_cache[k] = other._spatial_cache[k]
+                else:
+                    new_cache[k].update(other._spatial_cache[k])
+        return new_cache
+
+    def __neg__(self) -> 'SparseTensor':
+        return self.replace(-self.feats)
+    
+    def __elemwise__(self, other: Union[torch.Tensor, 'SparseTensor'], op: callable) -> 'SparseTensor':
+        if isinstance(other, torch.Tensor):
+            try:
+                other = torch.broadcast_to(other, self.shape)
+                other = sparse_batch_broadcast(self, other)
+            except:
+                pass
+        if isinstance(other, SparseTensor):
+            other = other.feats
+        new_feats = op(self.feats, other)
+        new_tensor = self.replace(new_feats)
+        if isinstance(other, SparseTensor):
+            new_tensor._spatial_cache = self.__merge_sparse_cache(other)
+        return new_tensor
+
+    def __add__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, torch.add)
+
+    def __radd__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, torch.add)
+    
+    def __sub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, torch.sub)
+    
+    def __rsub__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, lambda x, y: torch.sub(y, x))
+
+    def __mul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, torch.mul)
+
+    def __rmul__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, torch.mul)
+
+    def __truediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, torch.div)
+
+    def __rtruediv__(self, other: Union[torch.Tensor, 'SparseTensor', float]) -> 'SparseTensor':
+        return self.__elemwise__(other, lambda x, y: torch.div(y, x))
+
+    def __getitem__(self, idx):
+        if isinstance(idx, int):
+            idx = [idx]
+        elif isinstance(idx, slice):
+            idx = range(*idx.indices(self.shape[0]))
+        elif isinstance(idx, torch.Tensor):
+            if idx.dtype == torch.bool:
+                assert idx.shape == (self.shape[0],), f"Invalid index shape: {idx.shape}"
+                idx = idx.nonzero().squeeze(1)
+            elif idx.dtype in [torch.int32, torch.int64]:
+                assert len(idx.shape) == 1, f"Invalid index shape: {idx.shape}"
+            else:
+                raise ValueError(f"Unknown index type: {idx.dtype}")
+        else:
+            raise ValueError(f"Unknown index type: {type(idx)}")
+        
+        coords = []
+        feats = []
+        for new_idx, old_idx in enumerate(idx):
+            coords.append(self.coords[self.layout[old_idx]].clone())
+            coords[-1][:, 0] = new_idx
+            feats.append(self.feats[self.layout[old_idx]])
+        coords = torch.cat(coords, dim=0).contiguous()
+        feats = torch.cat(feats, dim=0).contiguous()
+        return SparseTensor(feats=feats, coords=coords)
+
+    def register_spatial_cache(self, key, value) -> None:
+        """
+        Register a spatial cache.
+        The spatial cache can be any thing you want to cache.
+        The registery and retrieval of the cache is based on current scale.
+        """
+        scale_key = str(self._scale)
+        if scale_key not in self._spatial_cache:
+            self._spatial_cache[scale_key] = {}
+        self._spatial_cache[scale_key][key] = value
+
+    def get_spatial_cache(self, key=None):
+        """
+        Get a spatial cache.
+        """
+        scale_key = str(self._scale)
+        cur_scale_cache = self._spatial_cache.get(scale_key, {})
+        if key is None:
+            return cur_scale_cache
+        return cur_scale_cache.get(key, None)
+
+
+def sparse_batch_broadcast(input: SparseTensor, other: torch.Tensor) -> torch.Tensor:
+    """
+    Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation.
+    
+    Args:
+        input (torch.Tensor): 1D tensor to broadcast.
+        target (SparseTensor): Sparse tensor to broadcast to.
+        op (callable): Operation to perform after broadcasting. Defaults to torch.add.
+    """
+    coords, feats = input.coords, input.feats
+    broadcasted = torch.zeros_like(feats)
+    for k in range(input.shape[0]):
+        broadcasted[input.layout[k]] = other[k]
+    return broadcasted
+
+
+def sparse_batch_op(input: SparseTensor, other: torch.Tensor, op: callable = torch.add) -> SparseTensor:
+    """
+    Broadcast a 1D tensor to a sparse tensor along the batch dimension then perform an operation.
+    
+    Args:
+        input (torch.Tensor): 1D tensor to broadcast.
+        target (SparseTensor): Sparse tensor to broadcast to.
+        op (callable): Operation to perform after broadcasting. Defaults to torch.add.
+    """
+    return input.replace(op(input.feats, sparse_batch_broadcast(input, other)))
+
+
+def sparse_cat(inputs: List[SparseTensor], dim: int = 0) -> SparseTensor:
+    """
+    Concatenate a list of sparse tensors.
+    
+    Args:
+        inputs (List[SparseTensor]): List of sparse tensors to concatenate.
+    """
+    if dim == 0:
+        start = 0
+        coords = []
+        for input in inputs:
+            coords.append(input.coords.clone())
+            coords[-1][:, 0] += start
+            start += input.shape[0]
+        coords = torch.cat(coords, dim=0)
+        feats = torch.cat([input.feats for input in inputs], dim=0)
+        output = SparseTensor(
+            coords=coords,
+            feats=feats,
+        )
+    else:
+        feats = torch.cat([input.feats for input in inputs], dim=dim)
+        output = inputs[0].replace(feats)
+
+    return output
+
+
+def sparse_unbind(input: SparseTensor, dim: int) -> List[SparseTensor]:
+    """
+    Unbind a sparse tensor along a dimension.
+    
+    Args:
+        input (SparseTensor): Sparse tensor to unbind.
+        dim (int): Dimension to unbind.
+    """
+    if dim == 0:
+        return [input[i] for i in range(input.shape[0])]
+    else:
+        feats = input.feats.unbind(dim)
+        return [input.replace(f) for f in feats]
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/__init__.py
new file mode 100644
index 0000000..0f7d367
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/__init__.py
@@ -0,0 +1,29 @@
+import os
+import logging
+from trellis.backend_config import get_sparse_backend, get_spconv_algo
+
+BACKEND = get_sparse_backend()
+SPCONV_ALGO = get_spconv_algo()
+
+def __from_env():
+    import os
+        
+    global SPCONV_ALGO
+    env_spconv_algo = os.environ.get('SPCONV_ALGO')
+    if env_spconv_algo is not None and env_spconv_algo in ['auto', 'implicit_gemm', 'native']:
+        SPCONV_ALGO = env_spconv_algo
+    print(f"[SPARSE][CONV] spconv algo: {SPCONV_ALGO}")
+        
+
+__from_env()
+
+if BACKEND == 'torchsparse':
+    from .conv_torchsparse import *
+elif BACKEND == 'spconv':
+    from .conv_spconv import *
+    
+__all__ = [
+    "SparseConv3d",
+    "SparseInverseConv3d",
+]
+
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/conv_spconv.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/conv_spconv.py
new file mode 100644
index 0000000..71dd5f0
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/conv_spconv.py
@@ -0,0 +1,85 @@
+import torch
+import torch.nn as nn
+from .. import SparseTensor
+from .. import DEBUG
+from trellis.backend_config import get_debug_mode, get_spconv_algo, get_sparse_backend
+
+# Get configuration from central config
+DEBUG = get_debug_mode()
+SPCONV_ALGO = get_spconv_algo()
+BACKEND = get_sparse_backend()
+
+class SparseConv3d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=None, bias=True, indice_key=None):
+        super(SparseConv3d, self).__init__()
+        if BACKEND == 'spconv':
+            import spconv.pytorch as spconv
+        algo = None
+        if SPCONV_ALGO == 'native':
+            algo = spconv.ConvAlgo.Native
+        elif SPCONV_ALGO == 'implicit_gemm':
+            algo = spconv.ConvAlgo.MaskImplicitGemm
+        if stride == 1 and (padding is None):
+            self.conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, dilation=dilation, bias=bias, indice_key=indice_key, algo=algo)
+        else:
+            self.conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, indice_key=indice_key, algo=algo)
+        self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride)
+        self.padding = padding
+
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        spatial_changed = any(s != 1 for s in self.stride) or (self.padding is not None)
+        new_data = self.conv(x.data)
+        new_shape = [x.shape[0], self.conv.out_channels]
+        new_layout = None if spatial_changed else x.layout
+
+        if spatial_changed and (x.shape[0] != 1):
+            # spconv was non-1 stride will break the contiguous of the output tensor, sort by the coords
+            fwd = new_data.indices[:, 0].argsort()
+            bwd = torch.zeros_like(fwd).scatter_(0, fwd, torch.arange(fwd.shape[0], device=fwd.device))
+            sorted_feats = new_data.features[fwd]
+            sorted_coords = new_data.indices[fwd]
+            unsorted_data = new_data
+            new_data = spconv.SparseConvTensor(sorted_feats, sorted_coords, unsorted_data.spatial_shape, unsorted_data.batch_size)  # type: ignore
+
+        out = SparseTensor(
+            new_data, shape=torch.Size(new_shape), layout=new_layout,
+            scale=tuple([s * stride for s, stride in zip(x._scale, self.stride)]),
+            spatial_cache=x._spatial_cache,
+        )
+
+        if spatial_changed and (x.shape[0] != 1):
+            out.register_spatial_cache(f'conv_{self.stride}_unsorted_data', unsorted_data)
+            out.register_spatial_cache(f'conv_{self.stride}_sort_bwd', bwd)
+ 
+        return out
+
+
+class SparseInverseConv3d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
+        super(SparseInverseConv3d, self).__init__()
+        if BACKEND == 'spconv':
+            import spconv.pytorch as spconv
+        self.conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, bias=bias, indice_key=indice_key)
+        self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride)
+
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        spatial_changed = any(s != 1 for s in self.stride)
+        if spatial_changed:
+            # recover the original spconv order
+            data = x.get_spatial_cache(f'conv_{self.stride}_unsorted_data')
+            bwd = x.get_spatial_cache(f'conv_{self.stride}_sort_bwd')
+            data = data.replace_feature(x.feats[bwd])
+            if DEBUG:
+                assert torch.equal(data.indices, x.coords[bwd]), 'Recover the original order failed'
+        else:
+            data = x.data
+
+        new_data = self.conv(data)
+        new_shape = [x.shape[0], self.conv.out_channels]
+        new_layout = None if spatial_changed else x.layout
+        out = SparseTensor(
+            new_data, shape=torch.Size(new_shape), layout=new_layout,
+            scale=tuple([s // stride for s, stride in zip(x._scale, self.stride)]),
+            spatial_cache=x._spatial_cache,
+        )
+        return out
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/conv_torchsparse.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/conv_torchsparse.py
new file mode 100644
index 0000000..606cec9
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/conv/conv_torchsparse.py
@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+from .. import SparseTensor
+from trellis.backend_config import get_debug_mode, get_spconv_algo, get_sparse_backend
+
+# Get configuration from central config
+DEBUG = get_debug_mode()
+SPCONV_ALGO = get_spconv_algo()
+BACKEND = get_sparse_backend()
+
+class SparseConv3d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
+        super(SparseConv3d, self).__init__()
+        if BACKEND == 'torchsparse':
+            import torchsparse
+        self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias)
+
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        out = self.conv(x.data)
+        new_shape = [x.shape[0], self.conv.out_channels]
+        out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
+        out._spatial_cache = x._spatial_cache
+        out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)])
+        return out
+
+
+class SparseInverseConv3d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
+        super(SparseInverseConv3d, self).__init__()
+        if BACKEND == 'torchsparse':
+            import torchsparse
+        self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True)
+
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        out = self.conv(x.data)        
+        new_shape = [x.shape[0], self.conv.out_channels]
+        out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
+        out._spatial_cache = x._spatial_cache
+        out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)])
+        return out
+
+
+
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/linear.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/linear.py
new file mode 100644
index 0000000..a854e77
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/linear.py
@@ -0,0 +1,15 @@
+import torch
+import torch.nn as nn
+from . import SparseTensor
+
+__all__ = [
+    'SparseLinear'
+]
+
+
+class SparseLinear(nn.Linear):
+    def __init__(self, in_features, out_features, bias=True):
+        super(SparseLinear, self).__init__(in_features, out_features, bias)
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        return input.replace(super().forward(input.feats))
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/nonlinearity.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/nonlinearity.py
new file mode 100644
index 0000000..f200098
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/nonlinearity.py
@@ -0,0 +1,35 @@
+import torch
+import torch.nn as nn
+from . import SparseTensor
+
+__all__ = [
+    'SparseReLU',
+    'SparseSiLU',
+    'SparseGELU',
+    'SparseActivation'
+]
+
+
+class SparseReLU(nn.ReLU):
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        return input.replace(super().forward(input.feats))
+    
+
+class SparseSiLU(nn.SiLU):
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        return input.replace(super().forward(input.feats))
+
+
+class SparseGELU(nn.GELU):
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        return input.replace(super().forward(input.feats))
+
+
+class SparseActivation(nn.Module):
+    def __init__(self, activation: nn.Module):
+        super().__init__()
+        self.activation = activation
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        return input.replace(self.activation(input.feats))
+    
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/norm.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/norm.py
new file mode 100644
index 0000000..6b38a36
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/norm.py
@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+from . import SparseTensor
+from . import DEBUG
+
+__all__ = [
+    'SparseGroupNorm',
+    'SparseLayerNorm',
+    'SparseGroupNorm32',
+    'SparseLayerNorm32',
+]
+
+
+class SparseGroupNorm(nn.GroupNorm):
+    def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
+        super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine)
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        nfeats = torch.zeros_like(input.feats)
+        for k in range(input.shape[0]):
+            if DEBUG:
+                assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch"
+            bfeats = input.feats[input.layout[k]]
+            bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
+            bfeats = super().forward(bfeats)
+            bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
+            nfeats[input.layout[k]] = bfeats
+        return input.replace(nfeats)
+
+
+class SparseLayerNorm(nn.LayerNorm):
+    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
+        super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine)
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        nfeats = torch.zeros_like(input.feats)
+        for k in range(input.shape[0]):
+            bfeats = input.feats[input.layout[k]]
+            bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
+            bfeats = super().forward(bfeats)
+            bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
+            nfeats[input.layout[k]] = bfeats
+        return input.replace(nfeats)
+
+
+class SparseGroupNorm32(SparseGroupNorm):
+    """
+    A GroupNorm layer that converts to float32 before the forward pass.
+    """
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        return super().forward(x.float()).type(x.dtype)
+
+class SparseLayerNorm32(SparseLayerNorm):
+    """
+    A LayerNorm layer that converts to float32 before the forward pass.
+    """
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        return super().forward(x.float()).type(x.dtype)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/spatial.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/spatial.py
new file mode 100644
index 0000000..ad71214
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/spatial.py
@@ -0,0 +1,110 @@
+from typing import *
+import torch
+import torch.nn as nn
+from . import SparseTensor
+
+__all__ = [
+    'SparseDownsample',
+    'SparseUpsample',
+    'SparseSubdivide'
+]
+
+
+class SparseDownsample(nn.Module):
+    """
+    Downsample a sparse tensor by a factor of `factor`.
+    Implemented as average pooling.
+    """
+    def __init__(self, factor: Union[int, Tuple[int, ...], List[int]]):
+        super(SparseDownsample, self).__init__()
+        self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        DIM = input.coords.shape[-1] - 1
+        factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM
+        assert DIM == len(factor), 'Input coordinates must have the same dimension as the downsample factor.'
+
+        coord = list(input.coords.unbind(dim=-1))
+        for i, f in enumerate(factor):
+            coord[i+1] = coord[i+1] // f
+
+        MAX = [coord[i+1].max().item() + 1 for i in range(DIM)]
+        OFFSET = torch.cumprod(torch.tensor(MAX[::-1]), 0).tolist()[::-1] + [1]
+        code = sum([c * o for c, o in zip(coord, OFFSET)])
+        code, idx = code.unique(return_inverse=True)
+
+        new_feats = torch.scatter_reduce(
+            torch.zeros(code.shape[0], input.feats.shape[1], device=input.feats.device, dtype=input.feats.dtype),
+            dim=0,
+            index=idx.unsqueeze(1).expand(-1, input.feats.shape[1]),
+            src=input.feats,
+            reduce='mean'
+        )
+        new_coords = torch.stack(
+            [code // OFFSET[0]] +
+            [(code // OFFSET[i+1]) % MAX[i] for i in range(DIM)],
+            dim=-1
+        )
+        out = SparseTensor(new_feats, new_coords, input.shape,)
+        out._scale = tuple([s // f for s, f in zip(input._scale, factor)])
+        out._spatial_cache = input._spatial_cache
+
+        out.register_spatial_cache(f'upsample_{factor}_coords', input.coords)
+        out.register_spatial_cache(f'upsample_{factor}_layout', input.layout)
+        out.register_spatial_cache(f'upsample_{factor}_idx', idx)
+
+        return out
+
+
+class SparseUpsample(nn.Module):
+    """
+    Upsample a sparse tensor by a factor of `factor`.
+    Implemented as nearest neighbor interpolation.
+    """
+    def __init__(self, factor: Union[int, Tuple[int, int, int], List[int]]):
+        super(SparseUpsample, self).__init__()
+        self.factor = tuple(factor) if isinstance(factor, (list, tuple)) else factor
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        DIM = input.coords.shape[-1] - 1
+        factor = self.factor if isinstance(self.factor, tuple) else (self.factor,) * DIM
+        assert DIM == len(factor), 'Input coordinates must have the same dimension as the upsample factor.'
+
+        new_coords = input.get_spatial_cache(f'upsample_{factor}_coords')
+        new_layout = input.get_spatial_cache(f'upsample_{factor}_layout')
+        idx = input.get_spatial_cache(f'upsample_{factor}_idx')
+        if any([x is None for x in [new_coords, new_layout, idx]]):
+            raise ValueError('Upsample cache not found. SparseUpsample must be paired with SparseDownsample.')
+        new_feats = input.feats[idx]
+        out = SparseTensor(new_feats, new_coords, input.shape, new_layout)
+        out._scale = tuple([s * f for s, f in zip(input._scale, factor)])
+        out._spatial_cache = input._spatial_cache
+        return out
+    
+class SparseSubdivide(nn.Module):
+    """
+    Upsample a sparse tensor by a factor of `factor`.
+    Implemented as nearest neighbor interpolation.
+    """
+    def __init__(self):
+        super(SparseSubdivide, self).__init__()
+
+    def forward(self, input: SparseTensor) -> SparseTensor:
+        DIM = input.coords.shape[-1] - 1
+        # upsample scale=2^DIM
+        n_cube = torch.ones([2] * DIM, device=input.device, dtype=torch.int)
+        n_coords = torch.nonzero(n_cube)
+        n_coords = torch.cat([torch.zeros_like(n_coords[:, :1]), n_coords], dim=-1)
+        factor = n_coords.shape[0]
+        assert factor == 2 ** DIM
+        # print(n_coords.shape)
+        new_coords = input.coords.clone()
+        new_coords[:, 1:] *= 2
+        new_coords = new_coords.unsqueeze(1) + n_coords.unsqueeze(0).to(new_coords.dtype)
+        
+        new_feats = input.feats.unsqueeze(1).expand(input.feats.shape[0], factor, *input.feats.shape[1:])
+        out = SparseTensor(new_feats.flatten(0, 1), new_coords.flatten(0, 1), input.shape)
+        out._scale = input._scale * 2
+        out._spatial_cache = input._spatial_cache
+        return out
+
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/__init__.py
new file mode 100644
index 0000000..b08b0d4
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/__init__.py
@@ -0,0 +1,2 @@
+from .blocks import *
+from .modulated import *
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/blocks.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/blocks.py
new file mode 100644
index 0000000..9d037a4
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/blocks.py
@@ -0,0 +1,151 @@
+from typing import *
+import torch
+import torch.nn as nn
+from ..basic import SparseTensor
+from ..linear import SparseLinear
+from ..nonlinearity import SparseGELU
+from ..attention import SparseMultiHeadAttention, SerializeMode
+from ...norm import LayerNorm32
+
+
+class SparseFeedForwardNet(nn.Module):
+    def __init__(self, channels: int, mlp_ratio: float = 4.0):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            SparseLinear(channels, int(channels * mlp_ratio)),
+            SparseGELU(approximate="tanh"),
+            SparseLinear(int(channels * mlp_ratio), channels),
+        )
+
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        return self.mlp(x)
+
+
+class SparseTransformerBlock(nn.Module):
+    """
+    Sparse Transformer block (MSA + FFN).
+    """
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full",
+        window_size: Optional[int] = None,
+        shift_sequence: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        serialize_mode: Optional[SerializeMode] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qkv_bias: bool = True,
+        ln_affine: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.attn = SparseMultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_sequence=shift_sequence,
+            shift_window=shift_window,
+            serialize_mode=serialize_mode,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.mlp = SparseFeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+
+    def _forward(self, x: SparseTensor) -> SparseTensor:
+        h = x.replace(self.norm1(x.feats))
+        h = self.attn(h)
+        x = x + h
+        h = x.replace(self.norm2(x.feats))
+        h = self.mlp(h)
+        x = x + h
+        return x
+
+    def forward(self, x: SparseTensor) -> SparseTensor:
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False)
+        else:
+            return self._forward(x)
+
+
+class SparseTransformerCrossBlock(nn.Module):
+    """
+    Sparse Transformer cross-attention block (MSA + MCA + FFN).
+    """
+    def __init__(
+        self,
+        channels: int,
+        ctx_channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full",
+        window_size: Optional[int] = None,
+        shift_sequence: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        serialize_mode: Optional[SerializeMode] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qk_rms_norm_cross: bool = False,
+        qkv_bias: bool = True,
+        ln_affine: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.self_attn = SparseMultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            type="self",
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_sequence=shift_sequence,
+            shift_window=shift_window,
+            serialize_mode=serialize_mode,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.cross_attn = SparseMultiHeadAttention(
+            channels,
+            ctx_channels=ctx_channels,
+            num_heads=num_heads,
+            type="cross",
+            attn_mode="full",
+            qkv_bias=qkv_bias,
+            qk_rms_norm=qk_rms_norm_cross,
+        )
+        self.mlp = SparseFeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+
+    def _forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor):
+        h = x.replace(self.norm1(x.feats))
+        h = self.self_attn(h)
+        x = x + h
+        h = x.replace(self.norm2(x.feats))
+        h = self.cross_attn(h, context)
+        x = x + h
+        h = x.replace(self.norm3(x.feats))
+        h = self.mlp(h)
+        x = x + h
+        return x
+
+    def forward(self, x: SparseTensor, context: torch.Tensor):
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False)
+        else:
+            return self._forward(x, context)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/modulated.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/modulated.py
new file mode 100644
index 0000000..4a84165
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/sparse/transformer/modulated.py
@@ -0,0 +1,166 @@
+from typing import *
+import torch
+import torch.nn as nn
+from ..basic import SparseTensor
+from ..attention import SparseMultiHeadAttention, SerializeMode
+from ...norm import LayerNorm32
+from .blocks import SparseFeedForwardNet
+
+
+class ModulatedSparseTransformerBlock(nn.Module):
+    """
+    Sparse Transformer block (MSA + FFN) with adaptive layer norm conditioning.
+    """
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full",
+        window_size: Optional[int] = None,
+        shift_sequence: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        serialize_mode: Optional[SerializeMode] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qkv_bias: bool = True,
+        share_mod: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.share_mod = share_mod
+        self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.attn = SparseMultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_sequence=shift_sequence,
+            shift_window=shift_window,
+            serialize_mode=serialize_mode,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.mlp = SparseFeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+        if not share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(channels, 6 * channels, bias=True)
+            )
+
+    def _forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor:
+        if self.share_mod:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1)
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
+        h = x.replace(self.norm1(x.feats))
+        h = h * (1 + scale_msa) + shift_msa
+        h = self.attn(h)
+        h = h * gate_msa
+        x = x + h
+        h = x.replace(self.norm2(x.feats))
+        h = h * (1 + scale_mlp) + shift_mlp
+        h = self.mlp(h)
+        h = h * gate_mlp
+        x = x + h
+        return x
+
+    def forward(self, x: SparseTensor, mod: torch.Tensor) -> SparseTensor:
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False)
+        else:
+            return self._forward(x, mod)
+
+
+class ModulatedSparseTransformerCrossBlock(nn.Module):
+    """
+    Sparse Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning.
+    """
+    def __init__(
+        self,
+        channels: int,
+        ctx_channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "full",
+        window_size: Optional[int] = None,
+        shift_sequence: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        serialize_mode: Optional[SerializeMode] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qk_rms_norm_cross: bool = False,
+        qkv_bias: bool = True,
+        share_mod: bool = False,
+
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.share_mod = share_mod
+        self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
+        self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.self_attn = SparseMultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            type="self",
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_sequence=shift_sequence,
+            shift_window=shift_window,
+            serialize_mode=serialize_mode,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.cross_attn = SparseMultiHeadAttention(
+            channels,
+            ctx_channels=ctx_channels,
+            num_heads=num_heads,
+            type="cross",
+            attn_mode="full",
+            qkv_bias=qkv_bias,
+            qk_rms_norm=qk_rms_norm_cross,
+        )
+        self.mlp = SparseFeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+        if not share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(channels, 6 * channels, bias=True)
+            )
+
+    def _forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor) -> SparseTensor:
+        if self.share_mod:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1)
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
+        h = x.replace(self.norm1(x.feats))
+        h = h * (1 + scale_msa) + shift_msa
+        h = self.self_attn(h)
+        h = h * gate_msa
+        x = x + h
+        h = x.replace(self.norm2(x.feats))
+        h = self.cross_attn(h, context)
+        x = x + h
+        h = x.replace(self.norm3(x.feats))
+        h = h * (1 + scale_mlp) + shift_mlp
+        h = self.mlp(h)
+        h = h * gate_mlp
+        x = x + h
+        return x
+
+    def forward(self, x: SparseTensor, mod: torch.Tensor, context: torch.Tensor) -> SparseTensor:
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, mod, context, use_reentrant=False)
+        else:
+            return self._forward(x, mod, context)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/spatial.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/spatial.py
new file mode 100644
index 0000000..79e268d
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/spatial.py
@@ -0,0 +1,48 @@
+import torch
+
+
+def pixel_shuffle_3d(x: torch.Tensor, scale_factor: int) -> torch.Tensor:
+    """
+    3D pixel shuffle.
+    """
+    B, C, H, W, D = x.shape
+    C_ = C // scale_factor**3
+    x = x.reshape(B, C_, scale_factor, scale_factor, scale_factor, H, W, D)
+    x = x.permute(0, 1, 5, 2, 6, 3, 7, 4)
+    x = x.reshape(B, C_, H*scale_factor, W*scale_factor, D*scale_factor)
+    return x
+
+
+def patchify(x: torch.Tensor, patch_size: int):
+    """
+    Patchify a tensor.
+
+    Args:
+        x (torch.Tensor): (N, C, *spatial) tensor
+        patch_size (int): Patch size
+    """
+    DIM = x.dim() - 2
+    for d in range(2, DIM + 2):
+        assert x.shape[d] % patch_size == 0, f"Dimension {d} of input tensor must be divisible by patch size, got {x.shape[d]} and {patch_size}"
+
+    x = x.reshape(*x.shape[:2], *sum([[x.shape[d] // patch_size, patch_size] for d in range(2, DIM + 2)], []))
+    x = x.permute(0, 1, *([2 * i + 3 for i in range(DIM)] + [2 * i + 2 for i in range(DIM)]))
+    x = x.reshape(x.shape[0], x.shape[1] * (patch_size ** DIM), *(x.shape[-DIM:]))
+    return x
+
+
+def unpatchify(x: torch.Tensor, patch_size: int):
+    """
+    Unpatchify a tensor.
+
+    Args:
+        x (torch.Tensor): (N, C, *spatial) tensor
+        patch_size (int): Patch size
+    """
+    DIM = x.dim() - 2
+    assert x.shape[1] % (patch_size ** DIM) == 0, f"Second dimension of input tensor must be divisible by patch size to unpatchify, got {x.shape[1]} and {patch_size ** DIM}"
+
+    x = x.reshape(x.shape[0], x.shape[1] // (patch_size ** DIM), *([patch_size] * DIM), *(x.shape[-DIM:]))
+    x = x.permute(0, 1, *(sum([[2 + DIM + i, 2 + i] for i in range(DIM)], [])))
+    x = x.reshape(x.shape[0], x.shape[1], *[x.shape[2 + 2 * i] * patch_size for i in range(DIM)])
+    return x
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/__init__.py
new file mode 100644
index 0000000..b08b0d4
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/__init__.py
@@ -0,0 +1,2 @@
+from .blocks import *
+from .modulated import *
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/blocks.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/blocks.py
new file mode 100644
index 0000000..c37eb7e
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/blocks.py
@@ -0,0 +1,182 @@
+from typing import *
+import torch
+import torch.nn as nn
+from ..attention import MultiHeadAttention
+from ..norm import LayerNorm32
+
+
+class AbsolutePositionEmbedder(nn.Module):
+    """
+    Embeds spatial positions into vector representations.
+    """
+    def __init__(self, channels: int, in_channels: int = 3):
+        super().__init__()
+        self.channels = channels
+        self.in_channels = in_channels
+        self.freq_dim = channels // in_channels // 2
+        self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim
+        self.freqs = 1.0 / (10000 ** self.freqs)
+        
+    def _sin_cos_embedding(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Create sinusoidal position embeddings.
+
+        Args:
+            x: a 1-D Tensor of N indices
+
+        Returns:
+            an (N, D) Tensor of positional embeddings.
+        """
+        self.freqs = self.freqs.to(x.device)
+        out = torch.outer(x, self.freqs)
+        out = torch.cat([torch.sin(out), torch.cos(out)], dim=-1)
+        return out
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x (torch.Tensor): (N, D) tensor of spatial positions
+        """
+        N, D = x.shape
+        assert D == self.in_channels, "Input dimension must match number of input channels"
+        embed = self._sin_cos_embedding(x.reshape(-1))
+        embed = embed.reshape(N, -1)
+        if embed.shape[1] < self.channels:
+            embed = torch.cat([embed, torch.zeros(N, self.channels - embed.shape[1], device=embed.device)], dim=-1)
+        return embed
+
+
+class FeedForwardNet(nn.Module):
+    def __init__(self, channels: int, mlp_ratio: float = 4.0):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(channels, int(channels * mlp_ratio)),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(int(channels * mlp_ratio), channels),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.mlp(x)
+
+
+class TransformerBlock(nn.Module):
+    """
+    Transformer block (MSA + FFN).
+    """
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "windowed"] = "full",
+        window_size: Optional[int] = None,
+        shift_window: Optional[int] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qkv_bias: bool = True,
+        ln_affine: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.attn = MultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_window=shift_window,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.mlp = FeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+
+    def _forward(self, x: torch.Tensor) -> torch.Tensor:
+        h = self.norm1(x)
+        h = self.attn(h)
+        x = x + h
+        h = self.norm2(x)
+        h = self.mlp(h)
+        x = x + h
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False)
+        else:
+            return self._forward(x)
+
+
+class TransformerCrossBlock(nn.Module):
+    """
+    Transformer cross-attention block (MSA + MCA + FFN).
+    """
+    def __init__(
+        self,
+        channels: int,
+        ctx_channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "windowed"] = "full",
+        window_size: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qk_rms_norm_cross: bool = False,
+        qkv_bias: bool = True,
+        ln_affine: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
+        self.self_attn = MultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            type="self",
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_window=shift_window,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.cross_attn = MultiHeadAttention(
+            channels,
+            ctx_channels=ctx_channels,
+            num_heads=num_heads,
+            type="cross",
+            attn_mode="full",
+            qkv_bias=qkv_bias,
+            qk_rms_norm=qk_rms_norm_cross,
+        )
+        self.mlp = FeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+
+    def _forward(self, x: torch.Tensor, context: torch.Tensor):
+        h = self.norm1(x)
+        h = self.self_attn(h)
+        x = x + h
+        h = self.norm2(x)
+        h = self.cross_attn(h, context)
+        x = x + h
+        h = self.norm3(x)
+        h = self.mlp(h)
+        x = x + h
+        return x
+
+    def forward(self, x: torch.Tensor, context: torch.Tensor):
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False)
+        else:
+            return self._forward(x, context)
+        
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/modulated.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/modulated.py
new file mode 100644
index 0000000..d4aeca0
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/transformer/modulated.py
@@ -0,0 +1,157 @@
+from typing import *
+import torch
+import torch.nn as nn
+from ..attention import MultiHeadAttention
+from ..norm import LayerNorm32
+from .blocks import FeedForwardNet
+
+
+class ModulatedTransformerBlock(nn.Module):
+    """
+    Transformer block (MSA + FFN) with adaptive layer norm conditioning.
+    """
+    def __init__(
+        self,
+        channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "windowed"] = "full",
+        window_size: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qkv_bias: bool = True,
+        share_mod: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.share_mod = share_mod
+        self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.attn = MultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_window=shift_window,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.mlp = FeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+        if not share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(channels, 6 * channels, bias=True)
+            )
+
+    def _forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor:
+        if self.share_mod:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1)
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
+        h = self.norm1(x)
+        h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
+        h = self.attn(h)
+        h = h * gate_msa.unsqueeze(1)
+        x = x + h
+        h = self.norm2(x)
+        h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
+        h = self.mlp(h)
+        h = h * gate_mlp.unsqueeze(1)
+        x = x + h
+        return x
+
+    def forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor:
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, mod, use_reentrant=False)
+        else:
+            return self._forward(x, mod)
+
+
+class ModulatedTransformerCrossBlock(nn.Module):
+    """
+    Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning.
+    """
+    def __init__(
+        self,
+        channels: int,
+        ctx_channels: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: Literal["full", "windowed"] = "full",
+        window_size: Optional[int] = None,
+        shift_window: Optional[Tuple[int, int, int]] = None,
+        use_checkpoint: bool = False,
+        use_rope: bool = False,
+        qk_rms_norm: bool = False,
+        qk_rms_norm_cross: bool = False,
+        qkv_bias: bool = True,
+        share_mod: bool = False,
+    ):
+        super().__init__()
+        self.use_checkpoint = use_checkpoint
+        self.share_mod = share_mod
+        self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
+        self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
+        self.self_attn = MultiHeadAttention(
+            channels,
+            num_heads=num_heads,
+            type="self",
+            attn_mode=attn_mode,
+            window_size=window_size,
+            shift_window=shift_window,
+            qkv_bias=qkv_bias,
+            use_rope=use_rope,
+            qk_rms_norm=qk_rms_norm,
+        )
+        self.cross_attn = MultiHeadAttention(
+            channels,
+            ctx_channels=ctx_channels,
+            num_heads=num_heads,
+            type="cross",
+            attn_mode="full",
+            qkv_bias=qkv_bias,
+            qk_rms_norm=qk_rms_norm_cross,
+        )
+        self.mlp = FeedForwardNet(
+            channels,
+            mlp_ratio=mlp_ratio,
+        )
+        if not share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(channels, 6 * channels, bias=True)
+            )
+
+    def _forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor):
+        if self.share_mod:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1)
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
+        h = self.norm1(x)
+        h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
+        h = self.self_attn(h)
+        h = h * gate_msa.unsqueeze(1)
+        x = x + h
+        h = self.norm2(x)
+        h = self.cross_attn(h, context)
+        x = x + h
+        h = self.norm3(x)
+        h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
+        h = self.mlp(h)
+        h = h * gate_mlp.unsqueeze(1)
+        x = x + h
+        return x
+
+    def forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor):
+        if self.use_checkpoint:
+            return torch.utils.checkpoint.checkpoint(self._forward, x, mod, context, use_reentrant=False)
+        else:
+            return self._forward(x, mod, context)
+        
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/modules/utils.py b/Gen_3D_Modules/Stable3DGen/trellis/modules/utils.py
new file mode 100644
index 0000000..f0afb1b
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/modules/utils.py
@@ -0,0 +1,54 @@
+import torch.nn as nn
+from ..modules import sparse as sp
+
+FP16_MODULES = (
+    nn.Conv1d,
+    nn.Conv2d,
+    nn.Conv3d,
+    nn.ConvTranspose1d,
+    nn.ConvTranspose2d,
+    nn.ConvTranspose3d,
+    nn.Linear,
+    sp.SparseConv3d,
+    sp.SparseInverseConv3d,
+    sp.SparseLinear,
+)
+
+def convert_module_to_f16(l):
+    """
+    Convert primitive modules to float16.
+    """
+    if isinstance(l, FP16_MODULES):
+        for p in l.parameters():
+            p.data = p.data.half()
+
+
+def convert_module_to_f32(l):
+    """
+    Convert primitive modules to float32, undoing convert_module_to_f16().
+    """
+    if isinstance(l, FP16_MODULES):
+        for p in l.parameters():
+            p.data = p.data.float()
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/__init__.py
new file mode 100644
index 0000000..f9e8548
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/__init__.py
@@ -0,0 +1,24 @@
+from . import samplers
+from .trellis_image_to_3d import TrellisImageTo3DPipeline
+
+
+def from_pretrained(path: str):
+    """
+    Load a pipeline from a model folder or a Hugging Face model hub.
+
+    Args:
+        path: The path to the model. Can be either local path or a Hugging Face model name.
+    """
+    import os
+    import json
+    is_local = os.path.exists(f"{path}/pipeline.json")
+
+    if is_local:
+        config_file = f"{path}/pipeline.json"
+    else:
+        from huggingface_hub import hf_hub_download
+        config_file = hf_hub_download(path, "pipeline.json")
+
+    with open(config_file, 'r') as f:
+        config = json.load(f)
+    return globals()[config['name']].from_pretrained(path)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/base.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/base.py
new file mode 100644
index 0000000..37eb906
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/base.py
@@ -0,0 +1,77 @@
+from typing import *
+import torch
+import torch.nn as nn
+from .. import models
+
+
+class Pipeline:
+    """
+    A base class for pipelines.
+    """
+    def __init__(
+        self,
+        models: dict[str, nn.Module] = None,
+    ):
+        if models is None:
+            return
+        self.models = models
+        for model in self.models.values():
+            model.eval()
+
+    @staticmethod
+    def from_pretrained(path: str) -> "Pipeline":
+        """
+        Load a pretrained model.
+        """
+        import os
+        import json
+        is_local = os.path.exists(f"{path}/pipeline.json")
+
+        if is_local:
+            config_file = f"{path}/pipeline.json"
+            with open(config_file, 'r') as f:
+                #print("Loading pipeline.json:")
+                args = json.load(f)['args']
+                #print("Models in pipeline.json:")
+                '''for k, v in args['models'].items():
+                    print(f"  {k}: {v}")'''
+
+            # For local paths, construct the models dict differently
+            _models = {
+                k: models.from_pretrained(os.path.join(path, v))
+                for k, v in args['models'].items()
+            }
+        else:
+            from huggingface_hub import hf_hub_download
+            config_file = hf_hub_download(path, "pipeline.json")
+            with open(config_file, 'r') as f:
+                args = json.load(f)['args']
+
+            _models = {
+                k: models.from_pretrained(os.path.join(path, v))
+                for k, v in args['models'].items()
+            }
+
+        new_pipeline = Pipeline(_models)
+        new_pipeline._pretrained_args = args
+        return new_pipeline
+
+    @property
+    def device(self) -> torch.device:
+        for model in self.models.values():
+            if hasattr(model, 'device'):
+                return model.device
+        for model in self.models.values():
+            if hasattr(model, 'parameters'):
+                return next(model.parameters()).device
+        raise RuntimeError("No device found.")
+
+    def to(self, device: torch.device) -> None:
+        for model in self.models.values():
+            model.to(device)
+
+    def cuda(self) -> None:
+        self.to(torch.device("cuda"))
+
+    def cpu(self) -> None:
+        self.to(torch.device("cpu"))
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/__init__.py
new file mode 100644
index 0000000..54d412f
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/__init__.py
@@ -0,0 +1,2 @@
+from .base import Sampler
+from .flow_euler import FlowEulerSampler, FlowEulerCfgSampler, FlowEulerGuidanceIntervalSampler
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/base.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/base.py
new file mode 100644
index 0000000..1966ce7
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/base.py
@@ -0,0 +1,20 @@
+from typing import *
+from abc import ABC, abstractmethod
+
+
+class Sampler(ABC):
+    """
+    A base class for samplers.
+    """
+
+    @abstractmethod
+    def sample(
+        self,
+        model,
+        **kwargs
+    ):
+        """
+        Sample from a model.
+        """
+        pass
+    
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/classifier_free_guidance_mixin.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/classifier_free_guidance_mixin.py
new file mode 100644
index 0000000..5701b25
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/classifier_free_guidance_mixin.py
@@ -0,0 +1,12 @@
+from typing import *
+
+
+class ClassifierFreeGuidanceSamplerMixin:
+    """
+    A mixin class for samplers that apply classifier-free guidance.
+    """
+
+    def _inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, **kwargs):
+        pred = super()._inference_model(model, x_t, t, cond, **kwargs)
+        neg_pred = super()._inference_model(model, x_t, t, neg_cond, **kwargs)
+        return (1 + cfg_strength) * pred - cfg_strength * neg_pred
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/flow_euler.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/flow_euler.py
new file mode 100644
index 0000000..b453d01
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/flow_euler.py
@@ -0,0 +1,205 @@
+from typing import *
+import torch
+import numpy as np
+from tqdm import tqdm
+import comfy.utils
+from easydict import EasyDict as edict
+from .base import Sampler
+from .classifier_free_guidance_mixin import ClassifierFreeGuidanceSamplerMixin
+from .guidance_interval_mixin import GuidanceIntervalSamplerMixin
+
+
+class FlowEulerSampler(Sampler):
+    """
+    Generate samples from a flow-matching model using Euler sampling.
+
+    Args:
+        sigma_min: The minimum scale of noise in flow.
+    """
+    def __init__(
+        self,
+        sigma_min: float,
+    ):
+        self.sigma_min = sigma_min
+
+    def _eps_to_xstart(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (x_t - (self.sigma_min + (1 - self.sigma_min) * t) * eps) / (1 - t)
+
+    def _xstart_to_eps(self, x_t, t, x_0):
+        assert x_t.shape == x_0.shape
+        return (x_t - (1 - t) * x_0) / (self.sigma_min + (1 - self.sigma_min) * t)
+
+    def _v_to_xstart_eps(self, x_t, t, v):
+        assert x_t.shape == v.shape
+        eps = (1 - t) * v + x_t
+        x_0 = (1 - self.sigma_min) * x_t - (self.sigma_min + (1 - self.sigma_min) * t) * v
+        return x_0, eps
+
+    def _inference_model(self, model, x_t, t, cond=None, **kwargs):
+        # Get model dtype for consistency
+        model_dtype = next(model.parameters()).dtype
+        t = torch.tensor([1000 * t] * x_t.shape[0], device=x_t.device, dtype=model_dtype)
+        return model(x_t, t, cond, **kwargs)
+
+    def _get_model_prediction(self, model, x_t, t, cond=None, **kwargs):
+        pred_v = self._inference_model(model, x_t, t, cond, **kwargs)
+        pred_x_0, pred_eps = self._v_to_xstart_eps(x_t=x_t, t=t, v=pred_v)
+        return pred_x_0, pred_eps, pred_v
+
+    @torch.no_grad()
+    def sample_once(
+        self,
+        model,
+        x_t,
+        t: float,
+        t_prev: float,
+        cond: Optional[Any] = None,
+        **kwargs
+    ):
+        """
+        Sample x_{t-1} from the model using Euler method.
+        
+        Args:
+            model: The model to sample from.
+            x_t: The [N x C x ...] tensor of noisy inputs at time t.
+            t: The current timestep.
+            t_prev: The previous timestep.
+            cond: conditional information.
+            **kwargs: Additional arguments for model inference.
+
+        Returns:
+            a dict containing the following
+            - 'pred_x_prev': x_{t-1}.
+            - 'pred_x_0': a prediction of x_0.
+        """
+        pred_x_0, pred_eps, pred_v = self._get_model_prediction(model, x_t, t, cond, **kwargs)
+        pred_x_prev = x_t - (t - t_prev) * pred_v
+        return edict({"pred_x_prev": pred_x_prev, "pred_x_0": pred_x_0})
+
+    @torch.no_grad()
+    def sample(
+        self,
+        model,
+        noise,
+        cond: Optional[Any] = None,
+        steps: int = 50,
+        rescale_t: float = 1.0,
+        verbose: bool = True,
+        **kwargs
+    ):
+        """
+        Generate samples from the model using Euler method.
+        
+        Args:
+            model: The model to sample from.
+            noise: The initial noise tensor.
+            cond: conditional information.
+            steps: The number of steps to sample.
+            rescale_t: The rescale factor for t.
+            verbose: If True, show a progress bar.
+            **kwargs: Additional arguments for model_inference.
+
+        Returns:
+            a dict containing the following
+            - 'samples': the model samples.
+            - 'pred_x_t': a list of prediction of x_t.
+            - 'pred_x_0': a list of prediction of x_0.
+        """
+        sample = noise
+        t_seq = np.linspace(1, 0, steps + 1)
+        t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq)
+        t_pairs = list((t_seq[i], t_seq[i + 1]) for i in range(steps))
+        ret = edict({"samples": None, "pred_x_t": [], "pred_x_0": []})
+        comfy_pbar = comfy.utils.ProgressBar(steps)
+        for i, (t, t_prev) in enumerate(tqdm(t_pairs, desc="Sampling", disable=not verbose)):
+            out = self.sample_once(model, sample, t, t_prev, cond, **kwargs)
+            sample = out.pred_x_prev
+            ret.pred_x_t.append(out.pred_x_prev)
+            ret.pred_x_0.append(out.pred_x_0)
+            comfy_pbar.update_absolute(i + 1)   
+
+        ret.samples = sample
+        return ret
+
+
+class FlowEulerCfgSampler(ClassifierFreeGuidanceSamplerMixin, FlowEulerSampler):
+    """
+    Generate samples from a flow-matching model using Euler sampling with classifier-free guidance.
+    """
+    @torch.no_grad()
+    def sample(
+        self,
+        model,
+        noise,
+        cond,
+        neg_cond,
+        steps: int = 50,
+        rescale_t: float = 1.0,
+        cfg_strength: float = 3.0,
+        verbose: bool = True,
+        **kwargs
+    ):
+        """
+        Generate samples from the model using Euler method.
+        
+        Args:
+            model: The model to sample from.
+            noise: The initial noise tensor.
+            cond: conditional information.
+            neg_cond: negative conditional information.
+            steps: The number of steps to sample.
+            rescale_t: The rescale factor for t.
+            cfg_strength: The strength of classifier-free guidance.
+            verbose: If True, show a progress bar.
+            **kwargs: Additional arguments for model_inference.
+
+        Returns:
+            a dict containing the following
+            - 'samples': the model samples.
+            - 'pred_x_t': a list of prediction of x_t.
+            - 'pred_x_0': a list of prediction of x_0.
+        """
+        return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, **kwargs)
+
+
+class FlowEulerGuidanceIntervalSampler(GuidanceIntervalSamplerMixin, FlowEulerSampler):
+    """
+    Generate samples from a flow-matching model using Euler sampling with classifier-free guidance and interval.
+    """
+    @torch.no_grad()
+    def sample(
+        self,
+        model,
+        noise,
+        cond,
+        neg_cond,
+        steps: int = 50,
+        rescale_t: float = 1.0,
+        cfg_strength: float = 3.0,
+        cfg_interval: Tuple[float, float] = (0.0, 1.0),
+        verbose: bool = True,
+        **kwargs
+    ):
+        """
+        Generate samples from the model using Euler method.
+        
+        Args:
+            model: The model to sample from.
+            noise: The initial noise tensor.
+            cond: conditional information.
+            neg_cond: negative conditional information.
+            steps: The number of steps to sample.
+            rescale_t: The rescale factor for t.
+            cfg_strength: The strength of classifier-free guidance.
+            cfg_interval: The interval for classifier-free guidance.
+            verbose: If True, show a progress bar.
+            **kwargs: Additional arguments for model_inference.
+
+        Returns:
+            a dict containing the following
+            - 'samples': the model samples.
+            - 'pred_x_t': a list of prediction of x_t.
+            - 'pred_x_0': a list of prediction of x_0.
+        """
+        return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, cfg_interval=cfg_interval, **kwargs)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/guidance_interval_mixin.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/guidance_interval_mixin.py
new file mode 100644
index 0000000..7074a4d
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/samplers/guidance_interval_mixin.py
@@ -0,0 +1,15 @@
+from typing import *
+
+
+class GuidanceIntervalSamplerMixin:
+    """
+    A mixin class for samplers that apply classifier-free guidance with interval.
+    """
+
+    def _inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, cfg_interval, **kwargs):
+        if cfg_interval[0] <= t <= cfg_interval[1]:
+            pred = super()._inference_model(model, x_t, t, cond, **kwargs)
+            neg_pred = super()._inference_model(model, x_t, t, neg_cond, **kwargs)
+            return (1 + cfg_strength) * pred - cfg_strength * neg_pred
+        else:
+            return super()._inference_model(model, x_t, t, cond, **kwargs)
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/pipelines/trellis_image_to_3d.py b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/trellis_image_to_3d.py
new file mode 100644
index 0000000..6213728
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/pipelines/trellis_image_to_3d.py
@@ -0,0 +1,581 @@
+# trellis_image_to_3d.py
+from typing import *
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from tqdm import tqdm
+from easydict import EasyDict as edict
+from torchvision import transforms
+from PIL import Image
+import rembg
+import gc
+from .base import Pipeline
+from . import samplers
+from ..modules import sparse as sp
+from ..representations import MeshExtractResult
+from contextlib import contextmanager
+from typing import Literal
+import folder_paths
+from stable_trellis_model_manager import TrellisModelManager as StableTrellisModelManager
+
+import os
+import logging
+
+logger = logging.getLogger("IF_Hi3DGen")
+
+class TrellisImageTo3DPipeline(Pipeline):
+    """
+    Pipeline for inferring Trellis image-to-3D models.
+
+    Args:
+        models (dict[str, nn.Module]): The models to use in the pipeline.
+        sparse_structure_sampler (samplers.Sampler): The sampler for the sparse structure.
+        slat_sampler (samplers.Sampler): The sampler for the structured latent.
+        slat_normalization (dict): The normalization parameters for the structured latent.
+        image_cond_model (str): The name of the image conditioning model.
+    """
+    def __init__(
+        self,
+        models: dict[str, nn.Module] = None,
+        sparse_structure_sampler: samplers.Sampler = None,
+        slat_sampler: samplers.Sampler = None,
+        slat_normalization: dict = None,
+        image_cond_model: str = None,
+    ):
+        if models is None:
+            return
+        super().__init__(models)
+        self._device = next(self.models['image_cond_model'].parameters()).device
+        self._models_cpu = {}
+        self.sparse_structure_sampler = sparse_structure_sampler
+        self.slat_sampler = slat_sampler
+        self.sparse_structure_sampler_params = {}
+        self.slat_sampler_params = {}
+        self.slat_normalization = slat_normalization
+        self.rembg_session = None
+        self._init_image_cond_model(image_cond_model)
+
+    @staticmethod
+    def from_pretrained(path: str, dinov2_model: str = None) -> "TrellisImageTo3DPipeline":
+        """
+        Load a pretrained model.
+
+        Args:
+            path (str): The path to the model. Can be either local path or a Hugging Face repository.
+            dinov2_model (str, optional): Override the DINOv2 model specified in config
+        """
+        pipeline = super(TrellisImageTo3DPipeline, TrellisImageTo3DPipeline).from_pretrained(path)
+        new_pipeline = TrellisImageTo3DPipeline()
+        new_pipeline.__dict__ = pipeline.__dict__
+        args = pipeline._pretrained_args
+
+        # Store model directory path
+        new_pipeline.model_dir = os.path.dirname(os.path.dirname(path))  # Go up two levels (past ckpts/)
+        
+        new_pipeline._models_cpu = {}
+
+        new_pipeline.sparse_structure_sampler = getattr(samplers, args['sparse_structure_sampler']['name'])(**args['sparse_structure_sampler']['args'])
+        new_pipeline.sparse_structure_sampler_params = args['sparse_structure_sampler']['params']
+
+        new_pipeline.slat_sampler = getattr(samplers, args['slat_sampler']['name'])(**args['slat_sampler']['args'])
+        new_pipeline.slat_sampler_params = args['slat_sampler']['params']
+
+        new_pipeline.slat_normalization = args['slat_normalization']
+
+        # Use user-specified model if provided, otherwise use config
+        model_name = dinov2_model or args['image_cond_model']
+        
+        try:
+            new_pipeline._init_image_cond_model(model_name)
+        except Exception as e:
+            logger.error(f"Error initializing image conditioning model: {str(e)}")
+            raise
+
+        return new_pipeline
+    
+    def _init_image_cond_model(self, name: str):
+        """
+        Initialize the image conditioning model.
+        """
+        if not hasattr(self, 'model_dir'):
+            raise AttributeError("Pipeline model_dir not set. Please ensure from_pretrained() is called first.")
+            
+        # Create model manager instance with proper config
+        config = getattr(self, 'config', {})
+        model_manager = StableTrellisModelManager(self.model_dir, config=config)
+        
+        try:
+            # This will handle downloading if needed
+            dinov2_model = model_manager.load_dinov2(name)
+            
+            # Ensure model is in consistent dtype based on config
+            if getattr(config, 'use_fp16', True):
+                dinov2_model = dinov2_model.half()
+            else:
+                dinov2_model = dinov2_model.float()
+                
+            self.models['image_cond_model'] = dinov2_model
+            
+            transform = transforms.Compose([
+                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            ])
+            self.image_cond_model_transform = transform
+            
+        except Exception as e:
+            logger.error(f"Error loading DINOv2 model: {str(e)}")
+            raise
+
+    def preprocess_image(self, input: Image.Image) -> Image.Image:
+        """
+        Preprocess the input image.
+        """
+        # if has alpha channel, use it directly; otherwise, remove background
+        has_alpha = False
+        if input.mode == 'RGBA':
+            alpha = np.array(input)[:, :, 3]
+            if not np.all(alpha == 255):
+                has_alpha = True
+        if has_alpha:
+            output = input
+        else:
+            input = input.convert('RGB')
+            max_size = max(input.size)
+            scale = min(1, 1024 / max_size)
+            if scale < 1:
+                input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS)
+            if getattr(self, 'rembg_session', None) is None:
+                self.rembg_session = rembg.new_session('u2net')
+            output = rembg.remove(input, session=self.rembg_session)
+        output_np = np.array(output)
+        alpha = output_np[:, :, 3]
+        bbox = np.argwhere(alpha > 0.8 * 255)
+        bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0])
+        center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2
+        size = max(bbox[2] - bbox[0], bbox[3] - bbox[1])
+        size = int(size * 1.2)
+        bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2
+        output = output.crop(bbox)  # type: ignore
+        output = output.resize((518, 518), Image.Resampling.LANCZOS)
+        output = np.array(output).astype(np.float32) / 255
+        output = output[:, :, :3] * output[:, :, 3:4]
+        output = Image.fromarray((output * 255).astype(np.uint8))
+        return output
+
+    @torch.no_grad()
+    def encode_image(self, image: Union[torch.Tensor, list[Image.Image]]) -> torch.Tensor:
+        """
+        Encode the image.
+
+        Args:
+            image (Union[torch.Tensor, list[Image.Image]]): The image to encode
+
+        Returns:
+            torch.Tensor: The encoded features.
+        """
+        if isinstance(image, torch.Tensor):
+            assert image.ndim == 4, "Image tensor should be batched (B, C, H, W)"
+        elif isinstance(image, list):
+            assert all(isinstance(i, Image.Image) for i in image), "Image list should be list of PIL images"
+            image = [i.resize((518, 518), Image.LANCZOS) for i in image]
+            image = [np.array(i.convert('RGB')).astype(np.float32) / 255 for i in image]
+            image = [torch.from_numpy(i).permute(2, 0, 1).float() for i in image]
+            image = torch.stack(image).to(self.device)
+        else:
+            raise ValueError(f"Unsupported type of image: {type(image)}")
+        
+        # Apply normalization and convert to model dtype
+        image = self.image_cond_model_transform(image)
+        model_dtype = next(self.models['image_cond_model'].parameters()).dtype
+        image = image.to(device=self.device, dtype=model_dtype)
+        
+        # Run model inference
+        features = self.models['image_cond_model'](image, is_training=True)['x_prenorm']
+        patchtokens = F.layer_norm(features, features.shape[-1:])
+        return patchtokens
+        
+    def get_cond(self, image: Union[torch.Tensor, list[Image.Image]]) -> dict:
+        """
+        Get the conditioning information for the model.
+
+        Args:
+            image (Union[torch.Tensor, list[Image.Image]]): The image prompts.
+
+        Returns:
+            dict: The conditioning information
+        """
+        cond = self.encode_image(image)
+        neg_cond = torch.zeros_like(cond)
+        return {
+            'cond': cond,
+            'neg_cond': neg_cond,
+        }
+
+    @property
+    def device(self) -> torch.device:
+        """Override device property to ensure it persists"""
+        if not hasattr(self, '_device'):
+            self._device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        return self._device
+
+    def cleanup(self):
+        """Explicit cleanup method that preserves essential properties"""
+        # Store current device before cleanup
+        current_device = self.device
+        
+        # Clear rembg session
+        if hasattr(self, 'rembg_session') and self.rembg_session is not None:
+            del self.rembg_session
+            self.rembg_session = None
+        
+        # Clear CPU models
+        if hasattr(self, '_models_cpu'):
+            for key in list(self._models_cpu.keys()):
+                del self._models_cpu[key]
+            self._models_cpu.clear()
+        
+        # Clear GPU models while preserving the models dict
+        if hasattr(self, 'models'):
+            for key in list(self.models.keys()):
+                del self.models[key]
+        
+        # Force memory cleanup
+        torch.cuda.empty_cache()
+        gc.collect()
+        
+        # Restore essential properties
+        self._device = current_device
+        if not hasattr(self, 'models'):
+            self.models = {}
+        if not hasattr(self, '_models_cpu'):
+            self._models_cpu = {}
+
+    def unload_models(self, model_keys: List[str]):
+        """Unload specific models from GPU memory"""
+        for key in model_keys:
+            if key in self.models:
+                self._models_cpu[key] = self.models[key].cpu()
+                del self.models[key]
+        torch.cuda.empty_cache()
+        gc.collect()
+
+    def load_models(self, model_keys: List[str]):
+        """Load specific models back to GPU"""
+        for key in model_keys:
+            if key in self._models_cpu:
+                self.models[key] = self._models_cpu[key].to(self.device)
+                # Convert to fp16 if pipeline was configured for it
+                if hasattr(self, '_use_fp16') and self._use_fp16 and hasattr(self.models[key], 'half'):
+                    self.models[key] = self.models[key].half()
+                del self._models_cpu[key]
+
+    @torch.no_grad()
+    def sample_sparse_structure(
+        self,
+        cond: dict,
+        num_samples: int = 1,
+        sampler_params: dict = {},
+    ) -> torch.Tensor:
+        """
+        Sample sparse structures with the given conditioning.
+        
+        Args:
+            cond (dict): The conditioning information.
+            num_samples (int): The number of samples to generate.
+            sampler_params (dict): Additional parameters for the sampler.
+        """
+        # Load required models
+        self.load_models(['sparse_structure_flow_model', 'sparse_structure_decoder'])
+
+        # Sample occupancy latent
+        flow_model = self.models['sparse_structure_flow_model']
+        reso = flow_model.resolution
+        
+        # Get model dtype for consistency
+        model_dtype = next(flow_model.parameters()).dtype
+        
+        noise = torch.randn(num_samples, flow_model.in_channels, reso, reso, reso).to(self.device, dtype=model_dtype)
+        
+        # Ensure conditioning tensors match model dtype
+        cond_converted = {}
+        for key, value in cond.items():
+            if isinstance(value, torch.Tensor):
+                cond_converted[key] = value.to(dtype=model_dtype)
+            else:
+                cond_converted[key] = value
+        
+        sampler_params = {**self.sparse_structure_sampler_params, **sampler_params}
+        z_s = self.sparse_structure_sampler.sample(
+            flow_model,
+            noise,
+            **cond_converted,
+            **sampler_params,
+            verbose=True
+        ).samples
+        
+        # Decode occupancy latent
+        decoder = self.models['sparse_structure_decoder']
+        coords = torch.argwhere(decoder(z_s)>0)[:, [0, 2, 3, 4]].int()
+
+        return coords
+
+    def decode_slat(
+        self,
+        slat: sp.SparseTensor,
+        formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
+    ) -> dict:
+        """
+        Decode the structured latent.
+
+        Args:
+            slat (sp.SparseTensor): The structured latent.
+            formats (List[str]): The formats to decode the structured latent to.
+
+        Returns:
+            dict: The decoded structured latent.
+        """
+        ret = {}
+        for format_type in formats:
+            # Load and process one format at a time
+            if format_type == 'mesh':
+                self.load_models(['slat_decoder_mesh'])
+                ret['mesh'] = self.models['slat_decoder_mesh'](slat)
+                self.unload_models(['slat_decoder_mesh'])
+                
+            elif format_type == 'gaussian':
+                self.load_models(['slat_decoder_gs'])
+                ret['gaussian'] = self.models['slat_decoder_gs'](slat)
+                self.unload_models(['slat_decoder_gs'])
+                
+            elif format_type == 'radiance_field':
+                self.load_models(['slat_decoder_rf'])
+                ret['radiance_field'] = self.models['slat_decoder_rf'](slat)
+                self.unload_models(['slat_decoder_rf'])
+            
+            # Force garbage collection after each format
+            torch.cuda.empty_cache()
+            gc.collect()
+            
+        return ret
+    
+    def sample_slat(
+        self,
+        cond: dict,
+        coords: torch.Tensor,
+        sampler_params: dict = {},
+    ) -> sp.SparseTensor:
+        """
+        Sample structured latent with the given conditioning.
+        
+        Args:
+            cond (dict): The conditioning information.
+            coords (torch.Tensor): The coordinates of the sparse structure.
+            sampler_params (dict): Additional parameters for the sampler.
+        """
+        # Load required models
+        self.load_models(['slat_flow_model'])
+        
+        # Sample structured latent
+        flow_model = self.models['slat_flow_model']
+        
+        # Get model dtype for consistency
+        model_dtype = next(flow_model.parameters()).dtype
+        
+        noise = sp.SparseTensor(
+            feats=torch.randn(coords.shape[0], flow_model.in_channels).to(self.device, dtype=model_dtype),
+            coords=coords,
+        )
+        
+        # Ensure conditioning tensors match model dtype
+        cond_converted = {}
+        for key, value in cond.items():
+            if isinstance(value, torch.Tensor):
+                cond_converted[key] = value.to(dtype=model_dtype)
+            else:
+                cond_converted[key] = value
+        
+        sampler_params = {**self.slat_sampler_params, **sampler_params}
+        slat = self.slat_sampler.sample(
+            flow_model,
+            noise,
+            **cond_converted,
+            **sampler_params,
+            verbose=True
+        ).samples
+
+        std = torch.tensor(self.slat_normalization['std'])[None].to(slat.device, dtype=slat.feats.dtype)
+        mean = torch.tensor(self.slat_normalization['mean'])[None].to(slat.device, dtype=slat.feats.dtype)
+        slat = slat * std + mean
+        
+        return slat
+
+    @torch.no_grad()
+    def run(
+        self,
+        image: Image.Image,
+        num_samples: int = 1,
+        seed: int = 42,
+        sparse_structure_sampler_params: dict = {},
+        slat_sampler_params: dict = {},
+        formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
+        preprocess_image: bool = True,
+    ) -> dict:
+        """
+        Run the pipeline.
+
+        Args:
+            image (Image.Image): The image prompt.
+            num_samples (int): The number of samples to generate.
+            sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler.
+            slat_sampler_params (dict): Additional parameters for the structured latent sampler.
+            preprocess_image (bool): Whether to preprocess the image.
+        """
+        if preprocess_image:
+            image = self.preprocess_image(image)
+            
+        # Load and process image conditioning
+        self.load_models(['image_cond_model'])
+        cond = self.get_cond([image])
+        self.unload_models(['image_cond_model'])
+        
+        torch.manual_seed(seed)
+        
+        # Generate sparse structure
+        coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params)
+        torch.cuda.empty_cache()
+        gc.collect()
+        
+        # Generate SLAT
+        slat = self.sample_slat(cond, coords, slat_sampler_params)
+        torch.cuda.empty_cache()
+        gc.collect()
+
+        # Unload all models but don't do full cleanup
+        self.unload_models(list(self.models.keys()))
+        
+        # Process formats one at a time
+        results = self.decode_slat(slat, formats)
+        torch.cuda.empty_cache()
+        gc.collect()
+        
+        return results
+
+    @contextmanager
+    def inject_sampler_multi_image(
+        self,
+        sampler_name: str,
+        num_images: int,
+        num_steps: int,
+        mode: Literal['stochastic', 'multidiffusion'] = 'stochastic',
+    ):
+        """
+        Inject a sampler with multiple images as condition.
+        
+        Args:
+            sampler_name (str): The name of the sampler to inject.
+            num_images (int): The number of images to condition on.
+            num_steps (int): The number of steps to run the sampler for.
+        """
+        sampler = getattr(self, sampler_name)
+        setattr(sampler, f'_old_inference_model', sampler._inference_model)
+
+        if mode == 'stochastic':
+            if num_images > num_steps:
+                print(f"\033[93mWarning: number of conditioning images is greater than number of steps for {sampler_name}. "
+                    "This may lead to performance degradation.\033[0m")
+
+            cond_indices = (np.arange(num_steps) % num_images).tolist()
+            def _new_inference_model(self, model, x_t, t, cond, **kwargs):
+                cond_idx = cond_indices.pop(0)
+                cond_i = cond[cond_idx:cond_idx+1]
+                return self._old_inference_model(model, x_t, t, cond=cond_i, **kwargs)
+        
+        elif mode == 'multidiffusion':
+            from .samplers import FlowEulerSampler
+            def _new_inference_model(self, model, x_t, t, cond, neg_cond, cfg_strength, cfg_interval, **kwargs):
+                if cfg_interval[0] <= t <= cfg_interval[1]:
+                    preds = []
+                    for i in range(len(cond)):
+                        preds.append(FlowEulerSampler._inference_model(self, model, x_t, t, cond[i:i+1], **kwargs))
+                    pred = sum(preds) / len(preds)
+                    neg_pred = FlowEulerSampler._inference_model(self, model, x_t, t, neg_cond, **kwargs)
+                    return (1 + cfg_strength) * pred - cfg_strength * neg_pred
+                else:
+                    preds = []
+                    for i in range(len(cond)):
+                        preds.append(FlowEulerSampler._inference_model(self, model, x_t, t, cond[i:i+1], **kwargs))
+                    pred = sum(preds) / len(preds)
+                    return pred
+            
+        else:
+            raise ValueError(f"Unsupported mode: {mode}")
+            
+        sampler._inference_model = _new_inference_model.__get__(sampler, type(sampler))
+
+        yield
+
+        sampler._inference_model = sampler._old_inference_model
+        delattr(sampler, f'_old_inference_model')
+
+    @torch.no_grad()
+    def run_multi_image(
+        self,
+        images: List[Image.Image],
+        num_samples: int = 1,
+        seed: int = 42,
+        sparse_structure_sampler_params: dict = {},
+        slat_sampler_params: dict = {},
+        formats: List[str] = ['mesh', 'gaussian', 'radiance_field'],
+        preprocess_image: bool = True,
+        mode: Literal['stochastic', 'multidiffusion'] = 'stochastic',
+    ) -> dict:
+        """
+        Run the pipeline with multiple images as condition
+
+        Args:
+            images (List[Image.Image]): The multi-view images of the assets
+            num_samples (int): The number of samples to generate.
+            sparse_structure_sampler_params (dict): Additional parameters for the sparse structure sampler.
+            slat_sampler_params (dict): Additional parameters for the structured latent sampler.
+            preprocess_image (bool): Whether to preprocess the image.
+        """
+        if preprocess_image:
+            images = [self.preprocess_image(image) for image in images]
+        cond = self.get_cond(images)
+        cond['neg_cond'] = cond['neg_cond'][:1]
+        torch.manual_seed(seed)
+        ss_steps = {**self.sparse_structure_sampler_params, **sparse_structure_sampler_params}.get('steps')
+        with self.inject_sampler_multi_image('sparse_structure_sampler', len(images), ss_steps, mode=mode):
+            coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params)
+        slat_steps = {**self.slat_sampler_params, **slat_sampler_params}.get('steps')
+        with self.inject_sampler_multi_image('slat_sampler', len(images), slat_steps, mode=mode):
+            slat = self.sample_slat(cond, coords, slat_sampler_params)
+        return self.decode_slat(slat, formats)
+
+    @contextmanager
+    def inference_context(self):
+        """Context manager for inference that handles device placement and cleanup."""
+        try:
+            # Move models to device if needed
+            if hasattr(self, '_models_cpu'):
+                for key in self._models_cpu:
+                    if key in self.models:
+                        continue
+                    self.models[key] = self._models_cpu[key].to(self._device)
+                    if hasattr(self.models[key], 'eval'):
+                        self.models[key].eval()
+
+            with torch.no_grad():
+                yield
+
+        finally:
+            # Cleanup
+            if hasattr(self, '_models_cpu'):
+                for key in list(self.models.keys()):
+                    if key in self._models_cpu:
+                        self._models_cpu[key] = self.models[key].cpu()
+                        del self.models[key]
+            
+            # Force memory cleanup
+            torch.cuda.empty_cache()
+            gc.collect()
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/representations/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/representations/__init__.py
new file mode 100644
index 0000000..784d13a
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/representations/__init__.py
@@ -0,0 +1 @@
+from .mesh import MeshExtractResult
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/__init__.py
new file mode 100644
index 0000000..38cf35c
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/__init__.py
@@ -0,0 +1 @@
+from .cube2mesh import SparseFeatures2Mesh, MeshExtractResult
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/cube2mesh.py b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/cube2mesh.py
new file mode 100644
index 0000000..7880436
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/cube2mesh.py
@@ -0,0 +1,143 @@
+import torch
+from ...modules.sparse import SparseTensor
+from easydict import EasyDict as edict
+from .utils_cube import *
+from .flexicube import FlexiCubes
+
+
+class MeshExtractResult:
+    def __init__(self,
+        vertices,
+        faces,
+        vertex_attrs=None,
+        res=64
+    ):
+        self.vertices = vertices
+        self.faces = faces.long()
+        self.vertex_attrs = vertex_attrs
+        self.face_normal = self.comput_face_normals(vertices, faces)
+        self.res = res
+        self.success = (vertices.shape[0] != 0 and faces.shape[0] != 0)
+
+        # training only
+        self.tsdf_v = None
+        self.tsdf_s = None
+        self.reg_loss = None
+        
+    def comput_face_normals(self, verts, faces):
+        i0 = faces[..., 0].long()
+        i1 = faces[..., 1].long()
+        i2 = faces[..., 2].long()
+
+        v0 = verts[i0, :]
+        v1 = verts[i1, :]
+        v2 = verts[i2, :]
+        face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
+        face_normals = torch.nn.functional.normalize(face_normals, dim=1)
+        # print(face_normals.min(), face_normals.max(), face_normals.shape)
+        return face_normals[:, None, :].repeat(1, 3, 1)
+                
+    def comput_v_normals(self, verts, faces):
+        i0 = faces[..., 0].long()
+        i1 = faces[..., 1].long()
+        i2 = faces[..., 2].long()
+
+        v0 = verts[i0, :]
+        v1 = verts[i1, :]
+        v2 = verts[i2, :]
+        face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
+        v_normals = torch.zeros_like(verts)
+        v_normals.scatter_add_(0, i0[..., None].repeat(1, 3), face_normals)
+        v_normals.scatter_add_(0, i1[..., None].repeat(1, 3), face_normals)
+        v_normals.scatter_add_(0, i2[..., None].repeat(1, 3), face_normals)
+
+        v_normals = torch.nn.functional.normalize(v_normals, dim=1)
+        return v_normals   
+
+
+class SparseFeatures2Mesh:
+    def __init__(self, device="cuda", res=64, use_color=True):
+        '''
+        a model to generate a mesh from sparse features structures using flexicube
+        '''
+        super().__init__()
+        self.device=device
+        self.res = res
+        self.mesh_extractor = FlexiCubes(device=device)
+        self.sdf_bias = -1.0 / res
+        verts, cube = construct_dense_grid(self.res, self.device)
+        self.reg_c = cube.to(self.device)
+        self.reg_v = verts.to(self.device)
+        self.use_color = use_color
+        self._calc_layout()
+    
+    def _calc_layout(self):
+        LAYOUTS = {
+            'sdf': {'shape': (8, 1), 'size': 8},
+            'deform': {'shape': (8, 3), 'size': 8 * 3},
+            'weights': {'shape': (21,), 'size': 21}
+        }
+        if self.use_color:
+            '''
+            6 channel color including normal map
+            '''
+            LAYOUTS['color'] = {'shape': (8, 6,), 'size': 8 * 6}
+        self.layouts = edict(LAYOUTS)
+        start = 0
+        for k, v in self.layouts.items():
+            v['range'] = (start, start + v['size'])
+            start += v['size']
+        self.feats_channels = start
+        
+    def get_layout(self, feats : torch.Tensor, name : str):
+        if name not in self.layouts:
+            return None
+        return feats[:, self.layouts[name]['range'][0]:self.layouts[name]['range'][1]].reshape(-1, *self.layouts[name]['shape'])
+    
+    def __call__(self, cubefeats : SparseTensor, training=False):
+        """
+        Generates a mesh based on the specified sparse voxel structures.
+        Args:
+            cube_attrs [Nx21] : Sparse Tensor attrs about cube weights
+            verts_attrs [Nx10] : [0:1] SDF [1:4] deform [4:7] color [7:10] normal 
+        Returns:
+            return the success tag and ni you loss, 
+        """
+        # add sdf bias to verts_attrs
+        coords = cubefeats.coords[:, 1:]
+        feats = cubefeats.feats
+        
+        sdf, deform, color, weights = [self.get_layout(feats, name) for name in ['sdf', 'deform', 'color', 'weights']]
+        sdf += self.sdf_bias
+        v_attrs = [sdf, deform, color] if self.use_color else [sdf, deform]
+        v_pos, v_attrs, reg_loss = sparse_cube2verts(coords, torch.cat(v_attrs, dim=-1), training=training)
+        v_attrs_d = get_dense_attrs(v_pos, v_attrs, res=self.res+1, sdf_init=True)
+        weights_d = get_dense_attrs(coords, weights, res=self.res, sdf_init=False)
+        if self.use_color:
+            sdf_d, deform_d, colors_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4], v_attrs_d[..., 4:]
+        else:
+            sdf_d, deform_d = v_attrs_d[..., 0], v_attrs_d[..., 1:4]
+            colors_d = None
+            
+        x_nx3 = get_defomed_verts(self.reg_v, deform_d, self.res)
+        
+        vertices, faces, L_dev, colors = self.mesh_extractor(
+            voxelgrid_vertices=x_nx3,
+            scalar_field=sdf_d,
+            cube_idx=self.reg_c,
+            resolution=self.res,
+            beta=weights_d[:, :12],
+            alpha=weights_d[:, 12:20],
+            gamma_f=weights_d[:, 20],
+            voxelgrid_colors=colors_d,
+            training=training)
+        
+        mesh = MeshExtractResult(vertices=vertices, faces=faces, vertex_attrs=colors, res=self.res)
+        if training:
+            if mesh.success:
+                reg_loss += L_dev.mean() * 0.5
+            reg_loss += (weights[:,:20]).abs().mean() * 0.2
+            mesh.reg_loss = reg_loss
+            mesh.tsdf_v = get_defomed_verts(v_pos, v_attrs[:, 1:4], self.res)
+            mesh.tsdf_s = v_attrs[:, 0]
+        return mesh
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/flexicube.py b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/flexicube.py
new file mode 100644
index 0000000..0cbe9ac
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/flexicube.py
@@ -0,0 +1,361 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+
+import torch
+from .tables import *
+
+__all__ = [
+    'FlexiCubes'
+]
+
+
+class FlexiCubes:
+    def __init__(self, device="cuda"):
+
+        self.device = device
+        self.dmc_table = torch.tensor(dmc_table, dtype=torch.long, device=device, requires_grad=False)
+        self.num_vd_table = torch.tensor(num_vd_table,
+                                         dtype=torch.long, device=device, requires_grad=False)
+        self.check_table = torch.tensor(
+            check_table,
+            dtype=torch.long, device=device, requires_grad=False)
+
+        self.tet_table = torch.tensor(tet_table, dtype=torch.long, device=device, requires_grad=False)
+        self.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=device, requires_grad=False)
+        self.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=device, requires_grad=False)
+        self.quad_split_train = torch.tensor(
+            [0, 1, 1, 2, 2, 3, 3, 0], dtype=torch.long, device=device, requires_grad=False)
+
+        self.cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [
+                                         1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.float, device=device)
+        self.cube_corners_idx = torch.pow(2, torch.arange(8, requires_grad=False))
+        self.cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6,
+                                       2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, device=device, requires_grad=False)
+
+        self.edge_dir_table = torch.tensor([0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1],
+                                           dtype=torch.long, device=device)
+        self.dir_faces_table = torch.tensor([
+            [[5, 4], [3, 2], [4, 5], [2, 3]],
+            [[5, 4], [1, 0], [4, 5], [0, 1]],
+            [[3, 2], [1, 0], [2, 3], [0, 1]]
+        ], dtype=torch.long, device=device)
+        self.adj_pairs = torch.tensor([0, 1, 1, 3, 3, 2, 2, 0], dtype=torch.long, device=device)
+
+    def __call__(self, voxelgrid_vertices, scalar_field, cube_idx, resolution, qef_reg_scale=1e-3,
+                 weight_scale=0.99, beta=None, alpha=None, gamma_f=None, voxelgrid_colors=None, training=False):
+        num_vertices = voxelgrid_vertices.shape[0]
+        num_cubes = cube_idx.shape[0]
+        surf_cubes, occ_fx8 = self._identify_surf_cubes(scalar_field, cube_idx)
+        if surf_cubes.sum() == 0:
+            return (
+                torch.zeros((0, 3), device=self.device),
+                torch.zeros((0, 3), dtype=torch.long, device=self.device),
+                torch.zeros((0), device=self.device),
+                torch.zeros((0, voxelgrid_colors.shape[-1]), device=self.device) if voxelgrid_colors is not None else None
+            )
+        beta, alpha, gamma_f = self._normalize_weights(
+            beta, alpha, gamma_f, surf_cubes, weight_scale)
+        
+        if voxelgrid_colors is not None:
+            voxelgrid_colors = torch.sigmoid(voxelgrid_colors)
+
+        case_ids = self._get_case_id(occ_fx8, surf_cubes, resolution)
+
+        surf_edges, idx_map, edge_counts, surf_edges_mask = self._identify_surf_edges(
+            scalar_field, cube_idx, surf_cubes
+        )
+
+        vd, L_dev, vd_gamma, vd_idx_map, vd_color = self._compute_vd(
+            voxelgrid_vertices, cube_idx[surf_cubes], surf_edges, scalar_field,
+            case_ids, beta, alpha, gamma_f, idx_map, qef_reg_scale, voxelgrid_colors)
+        vertices, faces, s_edges, edge_indices, vertices_color = self._triangulate(
+            scalar_field, surf_edges, vd, vd_gamma, edge_counts, idx_map,
+            vd_idx_map, surf_edges_mask, training, vd_color)
+        return vertices, faces, L_dev, vertices_color
+
+    def _compute_reg_loss(self, vd, ue, edge_group_to_vd, vd_num_edges):
+        """
+        Regularizer L_dev as in Equation 8
+        """
+        dist = torch.norm(ue - torch.index_select(input=vd, index=edge_group_to_vd, dim=0), dim=-1)
+        mean_l2 = torch.zeros_like(vd[:, 0])
+        mean_l2 = (mean_l2).index_add_(0, edge_group_to_vd, dist) / vd_num_edges.squeeze(1).float()
+        mad = (dist - torch.index_select(input=mean_l2, index=edge_group_to_vd, dim=0)).abs()
+        return mad
+
+    def _normalize_weights(self, beta, alpha, gamma_f, surf_cubes, weight_scale):
+        """
+        Normalizes the given weights to be non-negative. If input weights are None, it creates and returns a set of weights of ones.
+        """
+        n_cubes = surf_cubes.shape[0]
+
+        if beta is not None:
+            beta = (torch.tanh(beta) * weight_scale + 1)
+        else:
+            beta = torch.ones((n_cubes, 12), dtype=torch.float, device=self.device)
+
+        if alpha is not None:
+            alpha = (torch.tanh(alpha) * weight_scale + 1)
+        else:
+            alpha = torch.ones((n_cubes, 8), dtype=torch.float, device=self.device)
+
+        if gamma_f is not None:
+            gamma_f = torch.sigmoid(gamma_f) * weight_scale + (1 - weight_scale) / 2
+        else:
+            gamma_f = torch.ones((n_cubes), dtype=torch.float, device=self.device)
+
+        return beta[surf_cubes], alpha[surf_cubes], gamma_f[surf_cubes]
+
+    @torch.no_grad()
+    def _get_case_id(self, occ_fx8, surf_cubes, res):
+        """
+        Obtains the ID of topology cases based on cell corner occupancy. This function resolves the 
+        ambiguity in the Dual Marching Cubes (DMC) configurations as described in Section 1.3 of the 
+        supplementary material. It should be noted that this function assumes a regular grid.
+        """
+        case_ids = (occ_fx8[surf_cubes] * self.cube_corners_idx.to(self.device).unsqueeze(0)).sum(-1)
+
+        problem_config = self.check_table.to(self.device)[case_ids]
+        to_check = problem_config[..., 0] == 1
+        problem_config = problem_config[to_check]
+        if not isinstance(res, (list, tuple)):
+            res = [res, res, res]
+
+        # The 'problematic_configs' only contain configurations for surface cubes. Next, we construct a 3D array,
+        # 'problem_config_full', to store configurations for all cubes (with default config for non-surface cubes).
+        # This allows efficient checking on adjacent cubes.
+        problem_config_full = torch.zeros(list(res) + [5], device=self.device, dtype=torch.long)
+        vol_idx = torch.nonzero(problem_config_full[..., 0] == 0)  # N, 3
+        vol_idx_problem = vol_idx[surf_cubes][to_check]
+        problem_config_full[vol_idx_problem[..., 0], vol_idx_problem[..., 1], vol_idx_problem[..., 2]] = problem_config
+        vol_idx_problem_adj = vol_idx_problem + problem_config[..., 1:4]
+
+        within_range = (
+            vol_idx_problem_adj[..., 0] >= 0) & (
+            vol_idx_problem_adj[..., 0] < res[0]) & (
+            vol_idx_problem_adj[..., 1] >= 0) & (
+            vol_idx_problem_adj[..., 1] < res[1]) & (
+            vol_idx_problem_adj[..., 2] >= 0) & (
+            vol_idx_problem_adj[..., 2] < res[2])
+
+        vol_idx_problem = vol_idx_problem[within_range]
+        vol_idx_problem_adj = vol_idx_problem_adj[within_range]
+        problem_config = problem_config[within_range]
+        problem_config_adj = problem_config_full[vol_idx_problem_adj[..., 0],
+                                                 vol_idx_problem_adj[..., 1], vol_idx_problem_adj[..., 2]]
+        # If two cubes with cases C16 and C19 share an ambiguous face, both cases are inverted.
+        to_invert = (problem_config_adj[..., 0] == 1)
+        idx = torch.arange(case_ids.shape[0], device=self.device)[to_check][within_range][to_invert]
+        case_ids.index_put_((idx,), problem_config[to_invert][..., -1])
+        return case_ids
+
+    @torch.no_grad()
+    def _identify_surf_edges(self, scalar_field, cube_idx, surf_cubes):
+        """
+        Identifies grid edges that intersect with the underlying surface by checking for opposite signs. As each edge 
+        can be shared by multiple cubes, this function also assigns a unique index to each surface-intersecting edge 
+        and marks the cube edges with this index.
+        """
+        occ_n = scalar_field < 0
+        all_edges = cube_idx[surf_cubes][:, self.cube_edges].reshape(-1, 2)
+        unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True)
+
+        unique_edges = unique_edges.long()
+        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
+
+        surf_edges_mask = mask_edges[_idx_map]
+        counts = counts[_idx_map]
+
+        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=cube_idx.device) * -1
+        mapping[mask_edges] = torch.arange(mask_edges.sum(), device=cube_idx.device)
+        # Shaped as [number of cubes x 12 edges per cube]. This is later used to map a cube edge to the unique index
+        # for a surface-intersecting edge. Non-surface-intersecting edges are marked with -1.
+        idx_map = mapping[_idx_map]
+        surf_edges = unique_edges[mask_edges]
+        return surf_edges, idx_map, counts, surf_edges_mask
+
+    @torch.no_grad()
+    def _identify_surf_cubes(self, scalar_field, cube_idx):
+        """
+        Identifies grid cubes that intersect with the underlying surface by checking if the signs at 
+        all corners are not identical.
+        """
+        occ_n = scalar_field < 0
+        occ_fx8 = occ_n[cube_idx.reshape(-1)].reshape(-1, 8)
+        _occ_sum = torch.sum(occ_fx8, -1)
+        surf_cubes = (_occ_sum > 0) & (_occ_sum < 8)
+        return surf_cubes, occ_fx8
+
+    def _linear_interp(self, edges_weight, edges_x):
+        """
+        Computes the location of zero-crossings on 'edges_x' using linear interpolation with 'edges_weight'.
+        """
+        edge_dim = edges_weight.dim() - 2
+        assert edges_weight.shape[edge_dim] == 2
+        edges_weight = torch.cat([torch.index_select(input=edges_weight, index=torch.tensor(1, device=self.device), dim=edge_dim), -
+                                 torch.index_select(input=edges_weight, index=torch.tensor(0, device=self.device), dim=edge_dim)]
+                                 , edge_dim)
+        denominator = edges_weight.sum(edge_dim)
+        ue = (edges_x * edges_weight).sum(edge_dim) / denominator
+        return ue
+
+    def _solve_vd_QEF(self, p_bxnx3, norm_bxnx3, c_bx3, qef_reg_scale):
+        p_bxnx3 = p_bxnx3.reshape(-1, 7, 3)
+        norm_bxnx3 = norm_bxnx3.reshape(-1, 7, 3)
+        c_bx3 = c_bx3.reshape(-1, 3)
+        A = norm_bxnx3
+        B = ((p_bxnx3) * norm_bxnx3).sum(-1, keepdims=True)
+
+        A_reg = (torch.eye(3, device=p_bxnx3.device) * qef_reg_scale).unsqueeze(0).repeat(p_bxnx3.shape[0], 1, 1)
+        B_reg = (qef_reg_scale * c_bx3).unsqueeze(-1)
+        A = torch.cat([A, A_reg], 1)
+        B = torch.cat([B, B_reg], 1)
+        dual_verts = torch.linalg.lstsq(A, B).solution.squeeze(-1)
+        return dual_verts
+
+    def _compute_vd(self, voxelgrid_vertices, surf_cubes_fx8, surf_edges, scalar_field,
+                    case_ids, beta, alpha, gamma_f, idx_map, qef_reg_scale, voxelgrid_colors):
+        """
+        Computes the location of dual vertices as described in Section 4.2
+        """
+        alpha_nx12x2 = torch.index_select(input=alpha, index=self.cube_edges, dim=1).reshape(-1, 12, 2)
+        surf_edges_x = torch.index_select(input=voxelgrid_vertices, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 3)
+        surf_edges_s = torch.index_select(input=scalar_field, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 1)
+        zero_crossing = self._linear_interp(surf_edges_s, surf_edges_x)
+        
+        if voxelgrid_colors is not None:
+            C = voxelgrid_colors.shape[-1]
+            surf_edges_c = torch.index_select(input=voxelgrid_colors, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, C)
+
+        idx_map = idx_map.reshape(-1, 12)
+        num_vd = torch.index_select(input=self.num_vd_table, index=case_ids, dim=0)
+        edge_group, edge_group_to_vd, edge_group_to_cube, vd_num_edges, vd_gamma = [], [], [], [], []
+        
+        # if color is not None:
+        #     vd_color = []
+
+        total_num_vd = 0
+        vd_idx_map = torch.zeros((case_ids.shape[0], 12), dtype=torch.long, device=self.device, requires_grad=False)
+
+        for num in torch.unique(num_vd):
+            cur_cubes = (num_vd == num)  # consider cubes with the same numbers of vd emitted (for batching)
+            curr_num_vd = cur_cubes.sum() * num
+            curr_edge_group = self.dmc_table[case_ids[cur_cubes], :num].reshape(-1, num * 7)
+            curr_edge_group_to_vd = torch.arange(
+                curr_num_vd, device=self.device).unsqueeze(-1).repeat(1, 7) + total_num_vd
+            total_num_vd += curr_num_vd
+            curr_edge_group_to_cube = torch.arange(idx_map.shape[0], device=self.device)[
+                cur_cubes].unsqueeze(-1).repeat(1, num * 7).reshape_as(curr_edge_group)
+
+            curr_mask = (curr_edge_group != -1)
+            edge_group.append(torch.masked_select(curr_edge_group, curr_mask))
+            edge_group_to_vd.append(torch.masked_select(curr_edge_group_to_vd.reshape_as(curr_edge_group), curr_mask))
+            edge_group_to_cube.append(torch.masked_select(curr_edge_group_to_cube, curr_mask))
+            vd_num_edges.append(curr_mask.reshape(-1, 7).sum(-1, keepdims=True))
+            vd_gamma.append(torch.masked_select(gamma_f, cur_cubes).unsqueeze(-1).repeat(1, num).reshape(-1))
+            # if color is not None:
+            #     vd_color.append(color[cur_cubes].unsqueeze(1).repeat(1, num, 1).reshape(-1, 3))
+            
+        edge_group = torch.cat(edge_group)
+        edge_group_to_vd = torch.cat(edge_group_to_vd)
+        edge_group_to_cube = torch.cat(edge_group_to_cube)
+        vd_num_edges = torch.cat(vd_num_edges)
+        vd_gamma = torch.cat(vd_gamma)
+        # if color is not None:
+        #     vd_color = torch.cat(vd_color)
+        # else:
+        #     vd_color = None
+
+        vd = torch.zeros((total_num_vd, 3), device=self.device)
+        beta_sum = torch.zeros((total_num_vd, 1), device=self.device)
+
+        idx_group = torch.gather(input=idx_map.reshape(-1), dim=0, index=edge_group_to_cube * 12 + edge_group)
+
+        x_group = torch.index_select(input=surf_edges_x, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 3)
+        s_group = torch.index_select(input=surf_edges_s, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 1)
+        
+
+        zero_crossing_group = torch.index_select(
+            input=zero_crossing, index=idx_group.reshape(-1), dim=0).reshape(-1, 3)
+
+        alpha_group = torch.index_select(input=alpha_nx12x2.reshape(-1, 2), dim=0,
+                                            index=edge_group_to_cube * 12 + edge_group).reshape(-1, 2, 1)
+        ue_group = self._linear_interp(s_group * alpha_group, x_group)
+
+        beta_group = torch.gather(input=beta.reshape(-1), dim=0,
+                                    index=edge_group_to_cube * 12 + edge_group).reshape(-1, 1)
+        beta_sum = beta_sum.index_add_(0, index=edge_group_to_vd, source=beta_group)
+        vd = vd.index_add_(0, index=edge_group_to_vd, source=ue_group * beta_group) / beta_sum
+        
+        '''
+        interpolate colors use the same method as dual vertices
+        '''
+        if voxelgrid_colors is not None:
+            vd_color = torch.zeros((total_num_vd, C), device=self.device)
+            c_group = torch.index_select(input=surf_edges_c, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, C)
+            uc_group = self._linear_interp(s_group * alpha_group, c_group)
+            vd_color = vd_color.index_add_(0, index=edge_group_to_vd, source=uc_group * beta_group) / beta_sum
+        else:
+            vd_color = None
+        
+        L_dev = self._compute_reg_loss(vd, zero_crossing_group, edge_group_to_vd, vd_num_edges)
+
+        v_idx = torch.arange(vd.shape[0], device=self.device)  # + total_num_vd
+
+        vd_idx_map = (vd_idx_map.reshape(-1)).scatter(dim=0, index=edge_group_to_cube *
+                                                      12 + edge_group, src=v_idx[edge_group_to_vd])
+
+        return vd, L_dev, vd_gamma, vd_idx_map, vd_color
+
+    def _triangulate(self, scalar_field, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, vd_color):
+        """
+        Connects four neighboring dual vertices to form a quadrilateral. The quadrilaterals are then split into 
+        triangles based on the gamma parameter, as described in Section 4.3.
+        """
+        with torch.no_grad():
+            group_mask = (edge_counts == 4) & surf_edges_mask  # surface edges shared by 4 cubes.
+            group = idx_map.reshape(-1)[group_mask]
+            vd_idx = vd_idx_map[group_mask]
+            edge_indices, indices = torch.sort(group, stable=True)
+            quad_vd_idx = vd_idx[indices].reshape(-1, 4)
+
+            # Ensure all face directions point towards the positive SDF to maintain consistent winding.
+            s_edges = scalar_field[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1)].reshape(-1, 2)
+            flip_mask = s_edges[:, 0] > 0
+            quad_vd_idx = torch.cat((quad_vd_idx[flip_mask][:, [0, 1, 3, 2]],
+                                     quad_vd_idx[~flip_mask][:, [2, 3, 1, 0]]))
+
+        quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4)
+        gamma_02 = quad_gamma[:, 0] * quad_gamma[:, 2]
+        gamma_13 = quad_gamma[:, 1] * quad_gamma[:, 3]
+        if not training:
+            mask = (gamma_02 > gamma_13)
+            faces = torch.zeros((quad_gamma.shape[0], 6), dtype=torch.long, device=quad_vd_idx.device)
+            faces[mask] = quad_vd_idx[mask][:, self.quad_split_1]
+            faces[~mask] = quad_vd_idx[~mask][:, self.quad_split_2]
+            faces = faces.reshape(-1, 3)
+        else:
+            vd_quad = torch.index_select(input=vd, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3)
+            vd_02 = (vd_quad[:, 0] + vd_quad[:, 2]) / 2
+            vd_13 = (vd_quad[:, 1] + vd_quad[:, 3]) / 2
+            weight_sum = (gamma_02 + gamma_13) + 1e-8
+            vd_center = (vd_02 * gamma_02.unsqueeze(-1) + vd_13 * gamma_13.unsqueeze(-1)) / weight_sum.unsqueeze(-1)
+            
+            if vd_color is not None:
+                color_quad = torch.index_select(input=vd_color, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, vd_color.shape[-1])
+                color_02 = (color_quad[:, 0] + color_quad[:, 2]) / 2
+                color_13 = (color_quad[:, 1] + color_quad[:, 3]) / 2
+                color_center = (color_02 * gamma_02.unsqueeze(-1) + color_13 * gamma_13.unsqueeze(-1)) / weight_sum.unsqueeze(-1)
+                vd_color = torch.cat([vd_color, color_center])
+            
+            
+            vd_center_idx = torch.arange(vd_center.shape[0], device=self.device) + vd.shape[0]
+            vd = torch.cat([vd, vd_center])
+            faces = quad_vd_idx[:, self.quad_split_train].reshape(-1, 4, 2)
+            faces = torch.cat([faces, vd_center_idx.reshape(-1, 1, 1).repeat(1, 4, 1)], -1).reshape(-1, 3)
+        return vd, faces, s_edges, edge_indices, vd_color
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/tables.py b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/tables.py
new file mode 100644
index 0000000..7c02dd7
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/tables.py
@@ -0,0 +1,791 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
+dmc_table = [
+[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 5, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 4, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 8, 11, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 5, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 4, 5, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 5, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 5, 7, 8, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 7, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 9, 10, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 4, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 4, 5, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 4, 5, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 5, 7, 9, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 2, 5, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[2, 3, 5, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[8, 9, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 8, -1, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 4, 7, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 9, 10, 11, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 7, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 9, -1, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 8, 10, 11, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 5, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 5, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 8, 9, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 5, 7, 9, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 5, 7, 8, 10, 11], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[5, 7, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 8, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 1, 9, -1, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[1, 3, 8, 9, -1, -1, -1], [6, 7, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[4, 6, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
+[[0, 3, 4, 6, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
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+[[1, 3, 4, 6, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
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+]
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+2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2,
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+2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,
+1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,
+1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
+1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
+check_table = [
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+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 194],
+[1, -1, 0, 0, 193],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 164],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 161],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 152],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 145],
+[1, 0, 0, 1, 144],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 137],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 133],
+[1, 0, 1, 0, 132],
+[1, 1, 0, 0, 131],
+[1, 1, 0, 0, 130],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 100],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 98],
+[0, 0, 0, 0, 0],
+[1, 0, 0, 1, 96],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 88],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 82],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 74],
+[0, 0, 0, 0, 0],
+[1, 0, 1, 0, 72],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 70],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 67],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 65],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 56],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 52],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 44],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 1, 0, 0, 40],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 38],
+[1, 0, -1, 0, 37],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 33],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 28],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 26],
+[1, 0, 0, -1, 25],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, -1, 0, 0, 20],
+[0, 0, 0, 0, 0],
+[1, 0, -1, 0, 18],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 9],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[1, 0, 0, -1, 6],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0]
+]
+tet_table = [
+[-1, -1, -1, -1, -1, -1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[4, 4, 4, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, -1],
+[1, 1, 1, 1, 1, 1],
+[4, 4, 4, 4, 4, 4],
+[0, 4, 0, 4, 4, -1],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[5, 5, 5, 5, 5, 5],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, -1, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, -1, 2, 4, 4, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 4, 4, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 4, 2, 4, 4, 2],
+[0, 4, 0, 4, 4, 0],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 5, 2, 5, 5, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[0, 1, 1, -1, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[4, 1, 1, 4, 4, 1],
+[0, 1, 1, 0, 0, 1],
+[4, 0, 0, 4, 4, 0],
+[2, 2, 2, 2, 2, 2],
+[-1, 1, 1, 4, 4, 1],
+[0, 1, 1, 4, 4, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[5, 1, 1, 5, 5, 1],
+[0, 1, 1, 0, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[8, 8, 8, 8, 8, 8],
+[1, 1, 1, 4, 4, 1],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 4, 4, 1],
+[0, 4, 0, 4, 4, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 5, 5, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[5, 5, 5, 5, 5, 5],
+[6, 6, 6, 6, 6, 6],
+[6, -1, 0, 6, 0, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 1, 1, 6, 1, 6],
+[4, 4, 4, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[6, 4, -1, 6, 4, 6],
+[6, 4, 0, 6, 4, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 1, 1, 6, 1, 6],
+[5, 5, 5, 5, 5, 5],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 2, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 4, 2, 2, 4, 2],
+[0, 4, 0, 4, 4, 0],
+[2, 0, 2, 2, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[6, 1, 1, 6, -1, 6],
+[6, 1, 1, 6, 0, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 2, 2, 6, 2, 6],
+[4, 1, 1, 4, 4, 1],
+[0, 1, 1, 0, 0, 1],
+[4, 0, 0, 4, 4, 4],
+[2, 2, 2, 2, 2, 2],
+[6, 1, 1, 6, 4, 6],
+[6, 1, 1, 6, 4, 6],
+[6, 0, 0, 6, 0, 6],
+[6, 2, 2, 6, 2, 6],
+[5, 1, 1, 5, 5, 1],
+[0, 1, 1, 0, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[6, 6, 6, 6, 6, 6],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 4, 1],
+[0, 4, 0, 4, 4, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 5, 0, 5, 0, 5],
+[5, 5, 5, 5, 5, 5],
+[5, 5, 5, 5, 5, 5],
+[0, 5, 0, 5, 0, 5],
+[-1, 5, 0, 5, 0, 5],
+[1, 5, 1, 5, 1, 5],
+[4, 5, -1, 5, 4, 5],
+[0, 5, 0, 5, 0, 5],
+[4, 5, 0, 5, 4, 5],
+[1, 5, 1, 5, 1, 5],
+[4, 4, 4, 4, 4, 4],
+[0, 4, 0, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[6, 6, 6, 6, 6, 6],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 5, 2, 5, -1, 5],
+[0, 5, 0, 5, 0, 5],
+[2, 5, 2, 5, 0, 5],
+[1, 5, 1, 5, 1, 5],
+[2, 5, 2, 5, 4, 5],
+[0, 5, 0, 5, 0, 5],
+[2, 5, 2, 5, 4, 5],
+[1, 5, 1, 5, 1, 5],
+[2, 4, 2, 4, 4, 2],
+[0, 4, 0, 4, 4, 4],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 6, 2, 6, 6, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 0, 2, 0, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[0, 1, 1, 1, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[4, 1, 1, 1, 4, 1],
+[0, 1, 1, 1, 0, 1],
+[4, 0, 0, 4, 4, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[5, 5, 5, 5, 5, 5],
+[1, 1, 1, 1, 4, 1],
+[0, 0, 0, 0, 0, 0],
+[4, 0, 0, 4, 4, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[6, 0, 0, 6, 0, 6],
+[0, 0, 0, 0, 0, 0],
+[6, 6, 6, 6, 6, 6],
+[5, 5, 5, 5, 5, 5],
+[5, 5, 0, 5, 0, 5],
+[5, 5, 0, 5, 0, 5],
+[5, 5, 1, 5, 1, 5],
+[4, 4, 4, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 0, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[4, 4, 4, 4, 4, 4],
+[4, 4, 0, 4, 4, 4],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[8, 8, 8, 8, 8, 8],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 0, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 1, 1, 4, 4, 1],
+[2, 2, 2, 2, 2, 2],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 0, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[2, 4, 2, 4, 4, 2],
+[1, 1, 1, 1, 1, 1],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[2, 2, 2, 2, 2, 2],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[5, 5, 5, 5, 5, 5],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[4, 4, 4, 4, 4, 4],
+[1, 1, 1, 1, 1, 1],
+[0, 0, 0, 0, 0, 0],
+[0, 0, 0, 0, 0, 0],
+[12, 12, 12, 12, 12, 12]
+]
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/utils_cube.py b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/utils_cube.py
new file mode 100644
index 0000000..23913c9
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/representations/mesh/utils_cube.py
@@ -0,0 +1,61 @@
+import torch
+cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [
+        1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.int)
+cube_neighbor = torch.tensor([[1, 0, 0], [-1, 0, 0], [0, 1, 0], [0, -1, 0], [0, 0, 1], [0, 0, -1]])
+cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6,
+                2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, requires_grad=False)
+     
+def construct_dense_grid(res, device='cuda'):
+    '''construct a dense grid based on resolution'''
+    res_v = res + 1
+    vertsid = torch.arange(res_v ** 3, device=device)
+    coordsid = vertsid.reshape(res_v, res_v, res_v)[:res, :res, :res].flatten()
+    cube_corners_bias = (cube_corners[:, 0] * res_v + cube_corners[:, 1]) * res_v + cube_corners[:, 2]
+    cube_fx8 = (coordsid.unsqueeze(1) + cube_corners_bias.unsqueeze(0).to(device))
+    verts = torch.stack([vertsid // (res_v ** 2), (vertsid // res_v) % res_v, vertsid % res_v], dim=1)
+    return verts, cube_fx8
+
+
+def construct_voxel_grid(coords):
+    verts = (cube_corners.unsqueeze(0).to(coords) + coords.unsqueeze(1)).reshape(-1, 3)
+    verts_unique, inverse_indices = torch.unique(verts, dim=0, return_inverse=True)
+    cubes = inverse_indices.reshape(-1, 8)
+    return verts_unique, cubes
+
+
+def cubes_to_verts(num_verts, cubes, value, reduce='mean'):
+    """
+    Args:
+        cubes [Vx8] verts index for each cube
+        value [Vx8xM] value to be scattered
+    Operation:
+        reduced[cubes[i][j]][k] += value[i][k]
+    """
+    M = value.shape[2] # number of channels
+    reduced = torch.zeros(num_verts, M, device=cubes.device)
+    return torch.scatter_reduce(reduced, 0, 
+        cubes.unsqueeze(-1).expand(-1, -1, M).flatten(0, 1), 
+        value.flatten(0, 1), reduce=reduce, include_self=False)
+    
+def sparse_cube2verts(coords, feats, training=True):
+    new_coords, cubes = construct_voxel_grid(coords)
+    new_feats = cubes_to_verts(new_coords.shape[0], cubes, feats)
+    if training:
+        con_loss = torch.mean((feats - new_feats[cubes]) ** 2)
+    else:
+        con_loss = 0.0
+    return new_coords, new_feats, con_loss
+    
+
+def get_dense_attrs(coords : torch.Tensor, feats : torch.Tensor, res : int, sdf_init=True):
+    F = feats.shape[-1]
+    dense_attrs = torch.zeros([res] * 3 + [F], device=feats.device)
+    if sdf_init:
+        dense_attrs[..., 0] = 1 # initial outside sdf value
+    dense_attrs[coords[:, 0], coords[:, 1], coords[:, 2], :] = feats
+    return dense_attrs.reshape(-1, F)
+
+
+def get_defomed_verts(v_pos : torch.Tensor, deform : torch.Tensor, res):
+    return v_pos / res - 0.5 + (1 - 1e-8) / (res * 2) * torch.tanh(deform)
+        
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/utils/__init__.py b/Gen_3D_Modules/Stable3DGen/trellis/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/utils/_rasterization.py b/Gen_3D_Modules/Stable3DGen/trellis/utils/_rasterization.py
new file mode 100644
index 0000000..a5e3810
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/utils/_rasterization.py
@@ -0,0 +1,96 @@
+import torch
+from typing import Union, List, Optional, Tuple, Dict
+import nvdiffrast.torch as dr
+import utils3d
+class RastContext:
+    def __init__(self, backend='cuda'):
+        self.backend = backend
+
+def rasterize_triangle_faces(
+    ctx: RastContext,
+    vertices: torch.Tensor,
+    faces: torch.Tensor,
+    width: int,
+    height: int,
+    attr: torch.Tensor = None,
+    uv: torch.Tensor = None,
+    texture: torch.Tensor = None,
+    model: torch.Tensor = None,
+    view: torch.Tensor = None,
+    projection: torch.Tensor = None,
+    antialiasing: Union[bool, List[int]] = True,
+    diff_attrs: Union[None, List[int]] = None,
+) -> Dict[str, torch.Tensor]:
+    """
+    Rasterize a mesh with vertex attributes.
+    """
+    assert vertices.ndim == 3
+    assert faces.ndim == 2
+
+    # Handle vertices dimensions
+    if vertices.shape[-1] == 2:
+        vertices = torch.cat([vertices, torch.zeros_like(vertices[..., :1]), torch.ones_like(vertices[..., :1])], dim=-1)
+    elif vertices.shape[-1] == 3:
+        vertices = torch.cat([vertices, torch.ones_like(vertices[..., :1])], dim=-1)
+    elif vertices.shape[-1] == 4:
+        pass
+    else:
+        raise ValueError(f'Wrong shape of vertices: {vertices.shape}')
+    
+    # Calculate MVP matrix
+    mvp = projection if projection is not None else torch.eye(4, device=vertices.device)
+    if view is not None:
+        mvp = mvp @ view
+    if model is not None:
+        mvp = mvp @ model
+
+    # Transform vertices to clip space
+    pos_clip = vertices @ mvp.transpose(-1, -2)
+    faces = faces.contiguous()
+    if attr is not None:
+        attr = attr.contiguous()
+
+    # Rasterize
+    rast_out, rast_db = dr.rasterize(ctx.nvd_ctx, pos_clip, faces, resolution=[height, width], grad_db=True)
+
+    # Extract basic outputs
+    face_id = rast_out[..., 3].flip(1)
+    depth = rast_out[..., 2].flip(1)
+    mask = (face_id > 0).float()
+    depth = (depth * 0.5 + 0.5) * mask + (1.0 - mask)
+
+    ret = {
+        'depth': depth,
+        'mask': mask,
+        'face_id': face_id,
+    }
+
+    # Handle attribute interpolation
+    if attr is not None:
+        image, image_dr = dr.interpolate(attr, rast_out, faces, rast_db, diff_attrs=diff_attrs)
+        
+        if antialiasing == True:
+            image = dr.antialias(image, rast_out, pos_clip, faces)
+        elif isinstance(antialiasing, list):
+            aa_image = dr.antialias(image[..., antialiasing], rast_out, pos_clip, faces)
+            image[..., antialiasing] = aa_image
+
+        image = image.flip(1).permute(0, 3, 1, 2)
+        ret['image'] = image
+
+    # Handle UV mapping
+    if uv is not None:
+        uv_map, uv_map_dr = dr.interpolate(uv, rast_out, faces, rast_db, diff_attrs='all')
+        ret['uv'] = uv_map
+        ret['uv_dr'] = uv_map_dr
+
+        if texture is not None:
+            texture_map = dr.texture(texture, uv_map, uv_map_dr)
+            ret['texture'] = texture_map.flip(1).permute(0, 3, 1, 2)
+
+    # Handle derivatives
+    if diff_attrs is not None:
+        image_dr = image_dr.flip(1).permute(0, 3, 1, 2)
+        ret['image_dr'] = image_dr
+
+    return ret 
\ No newline at end of file
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/utils/general_utils.py b/Gen_3D_Modules/Stable3DGen/trellis/utils/general_utils.py
new file mode 100644
index 0000000..3b454d9
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/utils/general_utils.py
@@ -0,0 +1,187 @@
+import numpy as np
+import cv2
+import torch
+
+
+# Dictionary utils
+def _dict_merge(dicta, dictb, prefix=''):
+    """
+    Merge two dictionaries.
+    """
+    assert isinstance(dicta, dict), 'input must be a dictionary'
+    assert isinstance(dictb, dict), 'input must be a dictionary'
+    dict_ = {}
+    all_keys = set(dicta.keys()).union(set(dictb.keys()))
+    for key in all_keys:
+        if key in dicta.keys() and key in dictb.keys():
+            if isinstance(dicta[key], dict) and isinstance(dictb[key], dict):
+                dict_[key] = _dict_merge(dicta[key], dictb[key], prefix=f'{prefix}.{key}')
+            else:
+                raise ValueError(f'Duplicate key {prefix}.{key} found in both dictionaries. Types: {type(dicta[key])}, {type(dictb[key])}')
+        elif key in dicta.keys():
+            dict_[key] = dicta[key]
+        else:
+            dict_[key] = dictb[key]
+    return dict_
+
+
+def dict_merge(dicta, dictb):
+    """
+    Merge two dictionaries.
+    """
+    return _dict_merge(dicta, dictb, prefix='')
+
+
+def dict_foreach(dic, func, special_func={}):
+    """
+    Recursively apply a function to all non-dictionary leaf values in a dictionary.
+    """
+    assert isinstance(dic, dict), 'input must be a dictionary'
+    for key in dic.keys():
+        if isinstance(dic[key], dict):
+            dic[key] = dict_foreach(dic[key], func)
+        else:
+            if key in special_func.keys():
+                dic[key] = special_func[key](dic[key])
+            else:
+                dic[key] = func(dic[key])
+    return dic
+
+
+def dict_reduce(dicts, func, special_func={}):
+    """
+    Reduce a list of dictionaries. Leaf values must be scalars.
+    """
+    assert isinstance(dicts, list), 'input must be a list of dictionaries'
+    assert all([isinstance(d, dict) for d in dicts]), 'input must be a list of dictionaries'
+    assert len(dicts) > 0, 'input must be a non-empty list of dictionaries'
+    all_keys = set([key for dict_ in dicts for key in dict_.keys()])
+    reduced_dict = {}
+    for key in all_keys:
+        vlist = [dict_[key] for dict_ in dicts if key in dict_.keys()]
+        if isinstance(vlist[0], dict):
+            reduced_dict[key] = dict_reduce(vlist, func, special_func)
+        else:
+            if key in special_func.keys():
+                reduced_dict[key] = special_func[key](vlist)
+            else:
+                reduced_dict[key] = func(vlist)
+    return reduced_dict
+
+
+def dict_any(dic, func):
+    """
+    Recursively apply a function to all non-dictionary leaf values in a dictionary.
+    """
+    assert isinstance(dic, dict), 'input must be a dictionary'
+    for key in dic.keys():
+        if isinstance(dic[key], dict):
+            if dict_any(dic[key], func):
+                return True
+        else:
+            if func(dic[key]):
+                return True
+    return False
+
+
+def dict_all(dic, func):
+    """
+    Recursively apply a function to all non-dictionary leaf values in a dictionary.
+    """
+    assert isinstance(dic, dict), 'input must be a dictionary'
+    for key in dic.keys():
+        if isinstance(dic[key], dict):
+            if not dict_all(dic[key], func):
+                return False
+        else:
+            if not func(dic[key]):
+                return False
+    return True
+
+
+def dict_flatten(dic, sep='.'):
+    """
+    Flatten a nested dictionary into a dictionary with no nested dictionaries.
+    """
+    assert isinstance(dic, dict), 'input must be a dictionary'
+    flat_dict = {}
+    for key in dic.keys():
+        if isinstance(dic[key], dict):
+            sub_dict = dict_flatten(dic[key], sep=sep)
+            for sub_key in sub_dict.keys():
+                flat_dict[str(key) + sep + str(sub_key)] = sub_dict[sub_key]
+        else:
+            flat_dict[key] = dic[key]
+    return flat_dict
+
+
+def make_grid(images, nrow=None, ncol=None, aspect_ratio=None):
+    num_images = len(images)
+    if nrow is None and ncol is None:
+        if aspect_ratio is not None:
+            nrow = int(np.round(np.sqrt(num_images / aspect_ratio)))
+        else:
+            nrow = int(np.sqrt(num_images))
+        ncol = (num_images + nrow - 1) // nrow
+    elif nrow is None and ncol is not None:
+        nrow = (num_images + ncol - 1) // ncol
+    elif nrow is not None and ncol is None:
+        ncol = (num_images + nrow - 1) // nrow
+    else:
+        assert nrow * ncol >= num_images, 'nrow * ncol must be greater than or equal to the number of images'
+        
+    grid = np.zeros((nrow * images[0].shape[0], ncol * images[0].shape[1], images[0].shape[2]), dtype=images[0].dtype)
+    for i, img in enumerate(images):
+        row = i // ncol
+        col = i % ncol
+        grid[row * img.shape[0]:(row + 1) * img.shape[0], col * img.shape[1]:(col + 1) * img.shape[1]] = img
+    return grid
+
+
+def notes_on_image(img, notes=None):
+    img = np.pad(img, ((0, 32), (0, 0), (0, 0)), 'constant', constant_values=0)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    if notes is not None:
+        img = cv2.putText(img, notes, (0, img.shape[0] - 4), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return img
+
+
+def save_image_with_notes(img, path, notes=None):
+    """
+    Save an image with notes.
+    """
+    if isinstance(img, torch.Tensor):
+        img = img.cpu().numpy().transpose(1, 2, 0)
+    if img.dtype == np.float32 or img.dtype == np.float64:
+        img = np.clip(img * 255, 0, 255).astype(np.uint8)
+    img = notes_on_image(img, notes)
+    cv2.imwrite(path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+
+
+# debug utils
+
+def atol(x, y):
+    """
+    Absolute tolerance.
+    """
+    return torch.abs(x - y)
+
+
+def rtol(x, y):
+    """
+    Relative tolerance.
+    """
+    return torch.abs(x - y) / torch.clamp_min(torch.maximum(torch.abs(x), torch.abs(y)), 1e-12)
+
+
+# print utils
+def indent(s, n=4):
+    """
+    Indent a string.
+    """
+    lines = s.split('\n')
+    for i in range(1, len(lines)):
+        lines[i] = ' ' * n + lines[i]
+    return '\n'.join(lines)
+
diff --git a/Gen_3D_Modules/Stable3DGen/trellis/utils/random_utils.py b/Gen_3D_Modules/Stable3DGen/trellis/utils/random_utils.py
new file mode 100644
index 0000000..5b668c2
--- /dev/null
+++ b/Gen_3D_Modules/Stable3DGen/trellis/utils/random_utils.py
@@ -0,0 +1,30 @@
+import numpy as np
+
+PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53]
+
+def radical_inverse(base, n):
+    val = 0
+    inv_base = 1.0 / base
+    inv_base_n = inv_base
+    while n > 0:
+        digit = n % base
+        val += digit * inv_base_n
+        n //= base
+        inv_base_n *= inv_base
+    return val
+
+def halton_sequence(dim, n):
+    return [radical_inverse(PRIMES[dim], n) for dim in range(dim)]
+
+def hammersley_sequence(dim, n, num_samples):
+    return [n / num_samples] + halton_sequence(dim - 1, n)
+
+def sphere_hammersley_sequence(n, num_samples, offset=(0, 0), remap=False):
+    u, v = hammersley_sequence(2, n, num_samples)
+    u += offset[0] / num_samples
+    v += offset[1]
+    if remap:
+        u = 2 * u if u < 0.25 else 2 / 3 * u + 1 / 3
+    theta = np.arccos(1 - 2 * u) - np.pi / 2
+    phi = v * 2 * np.pi
+    return [phi, theta]
\ No newline at end of file
diff --git a/Gen_3D_Modules/TripoSG/LICENSE b/Gen_3D_Modules/TripoSG/LICENSE
new file mode 100644
index 0000000..92e3953
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/LICENSE
@@ -0,0 +1,80 @@
+TENCENT HUNYUAN FLASHVDM  COMMUNITY LICENSE AGREEMENT
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+a.	This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+b.	Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
+ 
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: November 5, 2024
+
+Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan FlashVDM. You agree not to use Tencent Hunyuan FlashVDM or Model Derivatives:
+1.	Outside the Territory;
+2.	In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
+3.	To harm Yourself or others;
+4.	To repurpose or distribute output from Tencent Hunyuan FlashVDM or any Model Derivatives to harm Yourself or others; 
+5.	To override or circumvent the safety guardrails and safeguards We have put in place;
+6.	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+7.	To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
+8.	To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
+9.	To intentionally defame, disparage or otherwise harass others;
+10.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+11.	To generate or disseminate personal identifiable information with the purpose of harming others;
+12.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+13.	To impersonate another individual without consent, authorization, or legal right;
+14.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+15.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+16.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+17.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+18.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+19.	For military purposes;
+20.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
\ No newline at end of file
diff --git a/Gen_3D_Modules/TripoSG/inference_utils.py b/Gen_3D_Modules/TripoSG/inference_utils.py
new file mode 100644
index 0000000..99f8a7e
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/inference_utils.py
@@ -0,0 +1,479 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import scipy.ndimage
+from skimage import measure
+from einops import repeat
+from diso import DiffDMC
+import torch.nn.functional as F
+
+from TripoSG.utils.typing import *
+
+def generate_dense_grid_points_gpu(bbox_min: torch.Tensor,
+                                   bbox_max: torch.Tensor,
+                                   octree_depth: int,
+                                   indexing: str = "ij"):
+    length = bbox_max - bbox_min
+    num_cells = 2 ** octree_depth
+    device = bbox_min.device
+    
+    x = torch.linspace(bbox_min[0], bbox_max[0], int(num_cells), dtype=torch.float16, device=device)
+    y = torch.linspace(bbox_min[1], bbox_max[1], int(num_cells), dtype=torch.float16, device=device)
+    z = torch.linspace(bbox_min[2], bbox_max[2], int(num_cells), dtype=torch.float16, device=device)
+    
+    xs, ys, zs = torch.meshgrid(x, y, z, indexing=indexing)
+    xyz = torch.stack((xs, ys, zs), dim=-1)
+    xyz = xyz.view(-1, 3)
+    grid_size = [int(num_cells), int(num_cells), int(num_cells)]
+
+    return xyz, grid_size, length
+
+def find_mesh_grid_coordinates_fast_gpu(occupancy_grid, n_limits=-1):
+    core_grid = occupancy_grid[1:-1, 1:-1, 1:-1]
+    occupied = core_grid > 0
+
+    neighbors_unoccupied = (
+        (occupancy_grid[:-2, :-2, :-2] < 0)
+        | (occupancy_grid[:-2, :-2, 1:-1] < 0)
+        | (occupancy_grid[:-2, :-2, 2:] < 0)  # x-1, y-1, z-1/0/1
+        | (occupancy_grid[:-2, 1:-1, :-2] < 0)
+        | (occupancy_grid[:-2, 1:-1, 1:-1] < 0)
+        | (occupancy_grid[:-2, 1:-1, 2:] < 0)  # x-1, y0, z-1/0/1
+        | (occupancy_grid[:-2, 2:, :-2] < 0)
+        | (occupancy_grid[:-2, 2:, 1:-1] < 0)
+        | (occupancy_grid[:-2, 2:, 2:] < 0)  # x-1, y+1, z-1/0/1
+        | (occupancy_grid[1:-1, :-2, :-2] < 0)
+        | (occupancy_grid[1:-1, :-2, 1:-1] < 0)
+        | (occupancy_grid[1:-1, :-2, 2:] < 0)  # x0, y-1, z-1/0/1
+        | (occupancy_grid[1:-1, 1:-1, :-2] < 0)
+        | (occupancy_grid[1:-1, 1:-1, 2:] < 0)  # x0, y0, z-1/1
+        | (occupancy_grid[1:-1, 2:, :-2] < 0)
+        | (occupancy_grid[1:-1, 2:, 1:-1] < 0)
+        | (occupancy_grid[1:-1, 2:, 2:] < 0)  # x0, y+1, z-1/0/1
+        | (occupancy_grid[2:, :-2, :-2] < 0)
+        | (occupancy_grid[2:, :-2, 1:-1] < 0)
+        | (occupancy_grid[2:, :-2, 2:] < 0)  # x+1, y-1, z-1/0/1
+        | (occupancy_grid[2:, 1:-1, :-2] < 0)
+        | (occupancy_grid[2:, 1:-1, 1:-1] < 0)
+        | (occupancy_grid[2:, 1:-1, 2:] < 0)  # x+1, y0, z-1/0/1
+        | (occupancy_grid[2:, 2:, :-2] < 0)
+        | (occupancy_grid[2:, 2:, 1:-1] < 0)
+        | (occupancy_grid[2:, 2:, 2:] < 0)  # x+1, y+1, z-1/0/1
+    )
+    core_mesh_coords = torch.nonzero(occupied & neighbors_unoccupied, as_tuple=False) + 1
+
+    if n_limits != -1 and core_mesh_coords.shape[0] > n_limits:
+        print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}")
+        ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True)
+        core_mesh_coords = core_mesh_coords[ind]
+
+    return core_mesh_coords
+
+def find_candidates_band(occupancy_grid: torch.Tensor, band_threshold: float, n_limits: int = -1) -> torch.Tensor:
+    """
+    Returns the coordinates of all voxels in the occupancy_grid where |value| < band_threshold.
+
+    Args:
+        occupancy_grid (torch.Tensor): A 3D tensor of SDF values.
+        band_threshold (float): The threshold below which |SDF| must be to include the voxel.
+        n_limits (int): Maximum number of points to return (-1 for no limit)
+
+    Returns:
+        torch.Tensor: A 2D tensor of coordinates (N x 3) where each row is [x, y, z].
+    """
+    core_grid = occupancy_grid[1:-1, 1:-1, 1:-1]  
+    # logits to sdf
+    core_grid = torch.sigmoid(core_grid) * 2 - 1  
+    # Create a boolean mask for all cells in the band
+    in_band = torch.abs(core_grid) < band_threshold
+
+    # Get coordinates of all voxels in the band
+    core_mesh_coords = torch.nonzero(in_band, as_tuple=False) + 1
+
+    if n_limits != -1 and core_mesh_coords.shape[0] > n_limits:
+        print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}")
+        ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True)
+        core_mesh_coords = core_mesh_coords[ind]
+
+    return core_mesh_coords 
+
+def expand_edge_region_fast(edge_coords, grid_size):
+    expanded_tensor = torch.zeros(grid_size, grid_size, grid_size, device='cuda', dtype=torch.float16, requires_grad=False)
+    expanded_tensor[edge_coords[:, 0], edge_coords[:, 1], edge_coords[:, 2]] = 1
+    if grid_size < 512:
+        kernel_size = 5
+        pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=2).squeeze()
+    else:
+        kernel_size = 3
+        pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=1).squeeze()
+    expanded_coords_low_res = torch.nonzero(pooled_tensor, as_tuple=False).to(torch.int16)
+
+    expanded_coords_high_res = torch.stack([
+        torch.cat((expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1)),
+        torch.cat((expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2+1, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2 + 1)),
+        torch.cat((expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1))
+    ], dim=1)
+
+    return expanded_coords_high_res
+
+def zoom_block(block, scale_factor, order=3):
+    block = block.astype(np.float32)
+    return scipy.ndimage.zoom(block, scale_factor, order=order)
+
+def parallel_zoom(occupancy_grid, scale_factor):
+    result = torch.nn.functional.interpolate(occupancy_grid.unsqueeze(0).unsqueeze(0), scale_factor=scale_factor)
+    return result.squeeze(0).squeeze(0)
+
+
+@torch.no_grad()
+def hierarchical_extract_geometry(geometric_func: Callable,
+                     device: torch.device,
+                     bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
+                     dense_octree_depth: int = 8,
+                     hierarchical_octree_depth: int = 9,
+                     ):
+    """
+
+    Args:
+        geometric_func:
+        device:
+        bounds:
+        dense_octree_depth:
+        hierarchical_octree_depth:
+    Returns:
+
+    """
+    if isinstance(bounds, float):
+        bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+
+    bbox_min = torch.tensor(bounds[0:3]).to(device)
+    bbox_max = torch.tensor(bounds[3:6]).to(device)
+    bbox_size = bbox_max - bbox_min
+
+    xyz_samples, grid_size, length = generate_dense_grid_points_gpu(
+        bbox_min=bbox_min,
+        bbox_max=bbox_max,
+        octree_depth=dense_octree_depth,
+        indexing="ij"
+    )
+    
+    print(f'step 1 query num: {xyz_samples.shape[0]}')
+    grid_logits = geometric_func(xyz_samples.unsqueeze(0)).to(torch.float16).view(grid_size[0], grid_size[1], grid_size[2])
+    # print(f'step 1 grid_logits shape: {grid_logits.shape}')
+    for i in range(hierarchical_octree_depth - dense_octree_depth):
+        curr_octree_depth = dense_octree_depth + i + 1
+        # upsample
+        grid_size = 2**curr_octree_depth
+        normalize_offset = grid_size / 2
+        high_res_occupancy = parallel_zoom(grid_logits, 2)
+
+        band_threshold = 1.0
+        edge_coords = find_candidates_band(grid_logits, band_threshold)
+        expanded_coords = expand_edge_region_fast(edge_coords, grid_size=int(grid_size/2)).to(torch.float16)
+        print(f'step {i+2} query num: {len(expanded_coords)}')
+        expanded_coords_norm = (expanded_coords - normalize_offset) * (abs(bounds[0]) / normalize_offset)
+
+        all_logits = None
+
+        all_logits = geometric_func(expanded_coords_norm.unsqueeze(0)).to(torch.float16)
+        all_logits = torch.cat([expanded_coords_norm, all_logits[0]], dim=1)
+        # print("all logits shape = ", all_logits.shape)
+
+        indices = all_logits[..., :3]
+        indices = indices * (normalize_offset / abs(bounds[0]))  + normalize_offset
+        indices = indices.type(torch.IntTensor)
+        values = all_logits[:, 3]
+        # breakpoint()
+        high_res_occupancy[indices[:, 0], indices[:, 1], indices[:, 2]] = values
+        grid_logits = high_res_occupancy
+        torch.cuda.empty_cache()
+    mesh_v_f = []
+    try:
+        print("final grids shape = ", grid_logits.shape)
+        vertices, faces, normals, _ = measure.marching_cubes(grid_logits.float().cpu().numpy(), 0, method="lewiner")
+        vertices = vertices / (2**hierarchical_octree_depth) * bbox_size.cpu().numpy() + bbox_min.cpu().numpy()
+        mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces))
+    except Exception as e:
+        print(e)
+        torch.cuda.empty_cache()
+        mesh_v_f = (None, None)
+
+    return [mesh_v_f]
+
+def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float):
+    """
+    Args:
+        input_tensor: shape [D, D, D], torch.float16
+        alpha: isosurface offset
+    Returns:
+        mask: shape [D, D, D], torch.int32
+    """
+    device = input_tensor.device
+    D = input_tensor.shape[0]
+    signed_val = 0.0
+
+    # add isosurface offset and exclude invalid value
+    val = input_tensor + alpha
+    valid_mask = val > -9000
+
+    # obtain neighbors
+    def get_neighbor(t, shift, axis):
+        if shift == 0:
+            return t.clone()
+
+        pad_dims = [0, 0, 0, 0, 0, 0]  # [x_front，x_back，y_front，y_back，z_front，z_back]
+
+        if axis == 0:  # x axis
+            pad_idx = 0 if shift > 0 else 1
+            pad_dims[pad_idx] = abs(shift)
+        elif axis == 1:  # y axis
+            pad_idx = 2 if shift > 0 else 3
+            pad_dims[pad_idx] = abs(shift)
+        elif axis == 2:  # z axis
+            pad_idx = 4 if shift > 0 else 5
+            pad_dims[pad_idx] = abs(shift)
+
+        # Apply padding with replication at boundaries
+        padded = F.pad(t.unsqueeze(0).unsqueeze(0), pad_dims[::-1], mode='replicate')
+
+        # Create dynamic slicing indices
+        slice_dims = [slice(None)] * 3
+        if axis == 0:  # x axis
+            if shift > 0:
+                slice_dims[0] = slice(shift, None)
+            else:
+                slice_dims[0] = slice(None, shift)
+        elif axis == 1:  # y axis
+            if shift > 0:
+                slice_dims[1] = slice(shift, None)
+            else:
+                slice_dims[1] = slice(None, shift)
+        elif axis == 2:  # z axis
+            if shift > 0:
+                slice_dims[2] = slice(shift, None)
+            else:
+                slice_dims[2] = slice(None, shift)
+
+        # Apply slicing and restore dimensions
+        padded = padded.squeeze(0).squeeze(0)
+        sliced = padded[slice_dims]
+        return sliced
+
+    # Get neighbors in all directions
+    left = get_neighbor(val, 1, axis=0)  # x axis
+    right = get_neighbor(val, -1, axis=0)
+    back = get_neighbor(val, 1, axis=1)  # y axis
+    front = get_neighbor(val, -1, axis=1)
+    down = get_neighbor(val, 1, axis=2)  # z axis
+    up = get_neighbor(val, -1, axis=2)
+
+    # Handle invalid boundary values
+    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)
+
+    # Calculate sign consistency
+    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)
+
+    # Check if all signs are consistent
+    same_sign = torch.all(neighbors_sign == sign, dim=0)
+
+    # Generate final mask
+    mask = (~same_sign).to(torch.int32)
+    return mask * valid_mask.to(torch.int32)
+
+
+def generate_dense_grid_points_2(
+    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
+
+@torch.no_grad()
+def flash_extract_geometry(
+    latents: torch.FloatTensor,
+    vae: Callable,
+    bounds: Union[Tuple[float], List[float], float] = 1.01,
+    num_chunks: int = 10000,
+    mc_level: float = 0.0,
+    octree_depth: int = 9,
+    min_resolution: int = 63,
+    mini_grid_num: int = 4,
+    **kwargs,
+):
+    geo_decoder = vae.decoder
+    device = latents.device
+    dtype = latents.dtype
+    # resolution to depth
+    octree_resolution = 2 ** octree_depth
+    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)
+
+
+    # 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_2(
+        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 range(0, xyz_samples.shape[0], num_batchs):
+        queries = xyz_samples[start: start + num_batchs, :]
+        batch = queries.shape[0]
+        batch_latents = repeat(latents.squeeze(0), "p c -> b p c", b=batch)
+        # geo_decoder.set_topk(True)
+        geo_decoder.set_topk(False)
+        logits = vae.decode(batch_latents, queries).sample
+        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)
+            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:
+                # geo_decoder.set_topk(input_grid)
+                geo_decoder.set_topk(False)
+                logits_grid = vae.decode(latents,next_points[:, start_num:start_num + sum_num]).sample
+                start_num = start_num + sum_num
+                logits_grid_list.append(logits_grid)
+                input_grid = [[grid_index], [count]]
+                sum_num = count
+        if sum_num > 0:
+            # geo_decoder.set_topk(input_grid)
+            geo_decoder.set_topk(False)
+            logits_grid = vae.decode(latents,next_points[:, start_num:start_num + sum_num]).sample
+            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')
+    torch.cuda.empty_cache()
+    mesh_v_f = []
+    grid_logits = grid_logits[0]
+    try:
+        print("final grids shape = ", grid_logits.shape)
+        dmc = DiffDMC(dtype=torch.float32).to(grid_logits.device)
+        sdf = -grid_logits / octree_resolution
+        sdf = sdf.to(torch.float32).contiguous()
+        vertices, faces = dmc(sdf, deform=None, return_quads=False, normalize=False)
+        vertices = vertices.detach().cpu().numpy()
+        faces = faces.detach().cpu().numpy()[:, ::-1]        
+        vertices = vertices / (2 ** octree_depth) * bbox_size + bbox_min
+        mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces))
+    except Exception as e:
+        print(e)
+        torch.cuda.empty_cache()
+        mesh_v_f = (None, None)
+
+    return [mesh_v_f]
\ No newline at end of file
diff --git a/Gen_3D_Modules/TripoSG/models/attention_processor.py b/Gen_3D_Modules/TripoSG/models/attention_processor.py
new file mode 100644
index 0000000..79379d6
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/attention_processor.py
@@ -0,0 +1,420 @@
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import apply_rotary_emb
+from diffusers.utils import logging
+from einops import rearrange
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class FlashTripoSGAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
+    used in the Tripo2DiT model. It applies a s normalization layer and rotary embedding on query and key vector.
+    """
+
+    def __init__(self, topk=True):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+        self.topk = topk
+
+    def qkv(self, attn, q, k, v, attn_mask, dropout_p, is_causal):
+        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 = F.scaled_dot_product_attention(q, k0, v0)
+        elif self.topk is False:
+            out = F.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, :]
+                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)
+                out = F.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 __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        # NOTE that tripo2 split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
+        # instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
+        if not attn.is_cross_attention:
+            qkv = torch.cat((query, key, value), dim=-1)
+            split_size = qkv.shape[-1] // attn.heads // 3
+            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+        else:
+            kv = torch.cat((key, value), dim=-1)
+            split_size = kv.shape[-1] // attn.heads // 2
+            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
+            key, value = torch.split(kv, split_size, dim=-1)
+
+        head_dim = key.shape[-1]
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # flashvdm topk
+        hidden_states = self.qkv(attn, query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)   
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+class TripoSGAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
+    used in the TripoSG model. It applies a s normalization layer and rotary embedding on query and key vector.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        # NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
+        # instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
+        if not attn.is_cross_attention:
+            qkv = torch.cat((query, key, value), dim=-1)
+            split_size = qkv.shape[-1] // attn.heads // 3
+            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+        else:
+            kv = torch.cat((key, value), dim=-1)
+            split_size = kv.shape[-1] // attn.heads // 2
+            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
+            key, value = torch.split(kv, split_size, dim=-1)
+
+        head_dim = key.shape[-1]
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class FusedTripoSGAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused
+    projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
+    query and key vector.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FusedTripoSGAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        # NOTE that pre-trained split heads first, then split qkv
+        if encoder_hidden_states is None:
+            qkv = attn.to_qkv(hidden_states)
+            split_size = qkv.shape[-1] // attn.heads // 3
+            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+        else:
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(
+                    encoder_hidden_states
+                )
+            query = attn.to_q(hidden_states)
+
+            kv = attn.to_kv(encoder_hidden_states)
+            split_size = kv.shape[-1] // attn.heads // 2
+            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
+            key, value = torch.split(kv, split_size, dim=-1)
+
+        head_dim = key.shape[-1]
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
diff --git a/Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py b/Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py
new file mode 100644
index 0000000..64a9ea2
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/autoencoders/__init__.py
@@ -0,0 +1 @@
+from .autoencoder_kl_triposg import TripoSGVAEModel
diff --git a/Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py b/Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py
new file mode 100644
index 0000000..f7ec6cd
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/autoencoders/autoencoder_kl_triposg.py
@@ -0,0 +1,536 @@
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import Attention, AttentionProcessor
+from diffusers.models.autoencoders.vae import DecoderOutput
+from diffusers.models.modeling_outputs import AutoencoderKLOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import FP32LayerNorm, LayerNorm
+from diffusers.utils import logging
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from einops import repeat
+# from torch_cluster import fps
+from tqdm import tqdm
+
+from ..attention_processor import FusedTripoSGAttnProcessor2_0, TripoSGAttnProcessor2_0, FlashTripoSGAttnProcessor2_0
+from ..embeddings import FrequencyPositionalEmbedding
+from ..transformers.triposg_transformer import DiTBlock
+from .vae import DiagonalGaussianDistribution
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class TripoSGEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        dim: int = 512,
+        num_attention_heads: int = 8,
+        num_layers: int = 8,
+    ):
+        super().__init__()
+
+        self.proj_in = nn.Linear(in_channels, dim, bias=True)
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=False,
+                    use_cross_attention=True,
+                    cross_attention_dim=dim,
+                    cross_attention_norm_type="layer_norm",
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )  # cross attention
+            ]
+            + [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=True,
+                    self_attention_norm_type="fp32_layer_norm",
+                    use_cross_attention=False,
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )
+                for _ in range(num_layers)  # self attention
+            ]
+        )
+
+        self.norm_out = LayerNorm(dim)
+
+    def forward(self, sample_1: torch.Tensor, sample_2: torch.Tensor):
+        hidden_states = self.proj_in(sample_1)
+        encoder_hidden_states = self.proj_in(sample_2)
+
+        for layer, block in enumerate(self.blocks):
+            if layer == 0:
+                hidden_states = block(
+                    hidden_states, encoder_hidden_states=encoder_hidden_states
+                )
+            else:
+                hidden_states = block(hidden_states)
+
+        hidden_states = self.norm_out(hidden_states)
+
+        return hidden_states
+
+
+class TripoSGDecoder(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 1,
+        dim: int = 512,
+        num_attention_heads: int = 8,
+        num_layers: int = 16,
+        grad_type: str = "analytical",
+        grad_interval: float = 0.001,
+    ):
+        super().__init__()
+
+        if grad_type not in ["numerical", "analytical"]:
+            raise ValueError(f"grad_type must be one of ['numerical', 'analytical']")
+        self.grad_type = grad_type
+        self.grad_interval = grad_interval
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=True,
+                    self_attention_norm_type="fp32_layer_norm",
+                    use_cross_attention=False,
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )
+                for _ in range(num_layers)  # self attention
+            ]
+            + [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=False,
+                    use_cross_attention=True,
+                    cross_attention_dim=dim,
+                    cross_attention_norm_type="layer_norm",
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )  # cross attention
+            ]
+        )
+
+        self.proj_query = nn.Linear(in_channels, dim, bias=True)
+
+        self.norm_out = LayerNorm(dim)
+        self.proj_out = nn.Linear(dim, out_channels, bias=True)
+
+    def set_topk(self, topk):
+        self.blocks[-1].set_topk(topk)
+
+    def set_flash_processor(self, processor):
+        self.blocks[-1].set_flash_processor(processor)
+
+    def query_geometry(
+        self,
+        model_fn: callable,
+        queries: torch.Tensor,
+        sample: torch.Tensor,
+        grad: bool = False,
+    ):
+        logits = model_fn(queries, sample)
+        if grad:
+            with torch.autocast(device_type="cuda", dtype=torch.float32):
+                if self.grad_type == "numerical":
+                    interval = self.grad_interval
+                    grad_value = []
+                    for offset in [
+                        (interval, 0, 0),
+                        (0, interval, 0),
+                        (0, 0, interval),
+                    ]:
+                        offset_tensor = torch.tensor(offset, device=queries.device)[
+                            None, :
+                        ]
+                        res_p = model_fn(queries + offset_tensor, sample)[..., 0]
+                        res_n = model_fn(queries - offset_tensor, sample)[..., 0]
+                        grad_value.append((res_p - res_n) / (2 * interval))
+                    grad_value = torch.stack(grad_value, dim=-1)
+                else:
+                    queries_d = torch.clone(queries)
+                    queries_d.requires_grad = True
+                    with torch.enable_grad():
+                        res_d = model_fn(queries_d, sample)
+                        grad_value = torch.autograd.grad(
+                            res_d,
+                            [queries_d],
+                            grad_outputs=torch.ones_like(res_d),
+                            create_graph=self.training,
+                        )[0]
+        else:
+            grad_value = None
+
+        return logits, grad_value
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        queries: torch.Tensor,
+        kv_cache: Optional[torch.Tensor] = None,
+    ):
+        if kv_cache is None:
+            hidden_states = sample
+            for _, block in enumerate(self.blocks[:-1]):
+                hidden_states = block(hidden_states)
+            kv_cache = hidden_states
+
+        # query grid logits by cross attention
+        def query_fn(q, kv):
+            q = self.proj_query(q)
+            l = self.blocks[-1](q, encoder_hidden_states=kv)
+            return self.proj_out(self.norm_out(l))
+
+        logits, grad = self.query_geometry(
+            query_fn, queries, kv_cache, grad=self.training
+        )
+        logits = logits * -1 if not isinstance(logits, Tuple) else logits[0] * -1
+
+        return logits, kv_cache
+
+
+class TripoSGVAEModel(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,  # NOTE xyz instead of feature dim
+        latent_channels: int = 64,
+        num_attention_heads: int = 8,
+        width_encoder: int = 512,
+        width_decoder: int = 1024,
+        num_layers_encoder: int = 8,
+        num_layers_decoder: int = 16,
+        embedding_type: str = "frequency",
+        embed_frequency: int = 8,
+        embed_include_pi: bool = False,
+    ):
+        super().__init__()
+
+        self.out_channels = 1
+
+        if embedding_type == "frequency":
+            self.embedder = FrequencyPositionalEmbedding(
+                num_freqs=embed_frequency,
+                logspace=True,
+                input_dim=in_channels,
+                include_pi=embed_include_pi,
+            )
+        else:
+            raise NotImplementedError(
+                f"Embedding type {embedding_type} is not supported."
+            )
+
+        self.encoder = TripoSGEncoder(
+            in_channels=in_channels + self.embedder.out_dim,
+            dim=width_encoder,
+            num_attention_heads=num_attention_heads,
+            num_layers=num_layers_encoder,
+        )
+        self.decoder = TripoSGDecoder(
+            in_channels=self.embedder.out_dim,
+            out_channels=self.out_channels,
+            dim=width_decoder,
+            num_attention_heads=num_attention_heads,
+            num_layers=num_layers_decoder,
+        )
+
+        self.quant = nn.Linear(width_encoder, latent_channels * 2, bias=True)
+        self.post_quant = nn.Linear(latent_channels, width_decoder, bias=True)
+
+        self.use_slicing = False
+        self.slicing_length = 1
+
+    def set_flash_decoder(self):
+        self.decoder.set_flash_processor(FlashTripoSGAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedTripoSGAttnProcessor2_0
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError(
+                    "`fuse_qkv_projections()` is not supported for models having added KV projections."
+                )
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+        self.set_attn_processor(FusedTripoSGAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(TripoSGAttnProcessor2_0())
+
+    def enable_slicing(self, slicing_length: int = 1) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+        self.slicing_length = slicing_length
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    def _sample_features(
+        self, x: torch.Tensor, num_tokens: int = 2048, seed: Optional[int] = None
+    ):
+        """
+        Sample points from features of the input point cloud.
+
+        Args:
+            x (torch.Tensor): The input point cloud. shape: (B, N, C)
+            num_tokens (int, optional): The number of points to sample. Defaults to 2048.
+            seed (Optional[int], optional): The random seed. Defaults to None.
+        """
+        rng = np.random.default_rng(seed)
+        indices = rng.choice(
+            x.shape[1], num_tokens * 4, replace=num_tokens * 4 > x.shape[1]
+        )
+        selected_points = x[:, indices]
+
+        batch_size, num_points, num_channels = selected_points.shape
+        flattened_points = selected_points.view(batch_size * num_points, num_channels)
+        batch_indices = (
+            torch.arange(batch_size).to(x.device).repeat_interleave(num_points)
+        )
+
+        # fps sampling
+        sampling_ratio = 1.0 / 4
+        sampled_indices = fps(
+            flattened_points[:, :3],
+            batch_indices,
+            ratio=sampling_ratio,
+            random_start=self.training,
+        )
+        sampled_points = flattened_points[sampled_indices].view(
+            batch_size, -1, num_channels
+        )
+
+        return sampled_points
+
+    def _encode(
+        self, x: torch.Tensor, num_tokens: int = 2048, seed: Optional[int] = None
+    ):
+        position_channels = self.config.in_channels
+        positions, features = x[..., :position_channels], x[..., position_channels:]
+        x_kv = torch.cat([self.embedder(positions), features], dim=-1)
+
+        sampled_x = self._sample_features(x, num_tokens, seed)
+        positions, features = (
+            sampled_x[..., :position_channels],
+            sampled_x[..., position_channels:],
+        )
+        x_q = torch.cat([self.embedder(positions), features], dim=-1)
+
+        x = self.encoder(x_q, x_kv)
+
+        x = self.quant(x)
+
+        return x
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.Tensor, return_dict: bool = True, **kwargs
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        """
+        Encode a batch of point features into latents.
+        """
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [
+                self._encode(x_slice, **kwargs)
+                for x_slice in x.split(self.slicing_length)
+            ]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self._encode(x, **kwargs)
+
+        posterior = DiagonalGaussianDistribution(h, feature_dim=-1)
+
+        if not return_dict:
+            return (posterior,)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def _decode(
+        self,
+        z: torch.Tensor,
+        sampled_points: torch.Tensor,
+        num_chunks: int = 50000,
+        to_cpu: bool = False,
+        return_dict: bool = True,
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        xyz_samples = sampled_points
+
+        z = self.post_quant(z)
+
+        num_points = xyz_samples.shape[1]
+        kv_cache = None
+        dec = []
+
+        for i in range(0, num_points, num_chunks):
+            queries = xyz_samples[:, i : i + num_chunks, :].to(z.device, dtype=z.dtype)
+            queries = self.embedder(queries)
+
+            z_, kv_cache = self.decoder(z, queries, kv_cache)
+            dec.append(z_ if not to_cpu else z_.cpu())
+
+        z = torch.cat(dec, dim=1)
+
+        if not return_dict:
+            return (z,)
+
+        return DecoderOutput(sample=z)
+
+    @apply_forward_hook
+    def decode(
+        self,
+        z: torch.Tensor,
+        sampled_points: torch.Tensor,
+        return_dict: bool = True,
+        **kwargs,
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [
+                self._decode(z_slice, p_slice, **kwargs).sample
+                for z_slice, p_slice in zip(
+                    z.split(self.slicing_length),
+                    sampled_points.split(self.slicing_length),
+                )
+            ]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z, sampled_points, **kwargs).sample
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+    def forward(self, x: torch.Tensor):
+        pass
diff --git a/Gen_3D_Modules/TripoSG/models/autoencoders/vae.py b/Gen_3D_Modules/TripoSG/models/autoencoders/vae.py
new file mode 100644
index 0000000..01aae60
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/autoencoders/vae.py
@@ -0,0 +1,69 @@
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+from diffusers.utils.torch_utils import randn_tensor
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(
+        self,
+        parameters: torch.Tensor,
+        deterministic: bool = False,
+        feature_dim: int = 1,
+    ):
+        self.parameters = parameters
+        self.feature_dim = feature_dim
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=feature_dim)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(
+                self.mean, device=self.parameters.device, dtype=self.parameters.dtype
+            )
+
+    def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:
+        # make sure sample is on the same device as the parameters and has same dtype
+        sample = randn_tensor(
+            self.mean.shape,
+            generator=generator,
+            device=self.parameters.device,
+            dtype=self.parameters.dtype,
+        )
+        x = self.mean + self.std * sample
+        return x
+
+    def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2, 3],
+                )
+
+    def nll(
+        self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]
+    ) -> torch.Tensor:
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
+
+    def mode(self) -> torch.Tensor:
+        return self.mean
diff --git a/Gen_3D_Modules/TripoSG/models/embeddings.py b/Gen_3D_Modules/TripoSG/models/embeddings.py
new file mode 100644
index 0000000..1fdb5ea
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/embeddings.py
@@ -0,0 +1,96 @@
+import torch
+import torch.nn as nn
+
+
+class FrequencyPositionalEmbedding(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
diff --git a/Gen_3D_Modules/TripoSG/models/transformers/__init__.py b/Gen_3D_Modules/TripoSG/models/transformers/__init__.py
new file mode 100644
index 0000000..952297c
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/transformers/__init__.py
@@ -0,0 +1,61 @@
+from typing import Callable, Optional
+
+from .triposg_transformer import TripoSGDiTModel
+
+
+def default_set_attn_proc_func(
+    name: str,
+    hidden_size: int,
+    cross_attention_dim: Optional[int],
+    ori_attn_proc: object,
+) -> object:
+    return ori_attn_proc
+
+
+def set_transformer_attn_processor(
+    transformer: TripoSGDiTModel,
+    set_self_attn_proc_func: Callable = default_set_attn_proc_func,
+    set_cross_attn_1_proc_func: Callable = default_set_attn_proc_func,
+    set_cross_attn_2_proc_func: Callable = default_set_attn_proc_func,
+    set_self_attn_module_names: Optional[list[str]] = None,
+    set_cross_attn_1_module_names: Optional[list[str]] = None,
+    set_cross_attn_2_module_names: Optional[list[str]] = None,
+) -> None:
+    do_set_processor = lambda name, module_names: (
+        any([name.startswith(module_name) for module_name in module_names])
+        if module_names is not None
+        else True
+    )  # prefix match
+
+    attn_procs = {}
+    for name, attn_processor in transformer.attn_processors.items():
+        hidden_size = transformer.config.width
+        if name.endswith("attn1.processor"):
+            # self attention
+            attn_procs[name] = (
+                set_self_attn_proc_func(name, hidden_size, None, attn_processor)
+                if do_set_processor(name, set_self_attn_module_names)
+                else attn_processor
+            )
+        elif name.endswith("attn2.processor"):
+            # cross attention
+            cross_attention_dim = transformer.config.cross_attention_dim
+            attn_procs[name] = (
+                set_cross_attn_1_proc_func(
+                    name, hidden_size, cross_attention_dim, attn_processor
+                )
+                if do_set_processor(name, set_cross_attn_1_module_names)
+                else attn_processor
+            )
+        elif name.endswith("attn2_2.processor"):
+            # cross attention 2
+            cross_attention_dim = transformer.config.cross_attention_2_dim
+            attn_procs[name] = (
+                set_cross_attn_2_proc_func(
+                    name, hidden_size, cross_attention_dim, attn_processor
+                )
+                if do_set_processor(name, set_cross_attn_2_module_names)
+                else attn_processor
+            )
+
+    transformer.set_attn_processor(attn_procs)
diff --git a/Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py b/Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py
new file mode 100644
index 0000000..0928fa0
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/transformers/modeling_outputs.py
@@ -0,0 +1,8 @@
+from dataclasses import dataclass
+
+import torch
+
+
+@dataclass
+class Transformer1DModelOutput:
+    sample: torch.FloatTensor
diff --git a/Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py b/Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py
new file mode 100644
index 0000000..5676a11
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/models/transformers/triposg_transformer.py
@@ -0,0 +1,775 @@
+# Copyright (c) 2025 VAST-AI-Research and contributors
+
+# This code is based on Tencent HunyuanDiT (https://huggingface.co/Tencent-Hunyuan/HunyuanDiT),
+# which is licensed under the Tencent Hunyuan Community License Agreement.
+# Portions of this code are copied or adapted from HunyuanDiT.
+# See the original license below:
+
+# ---- Start of Tencent Hunyuan Community License Agreement ----
+
+# TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
+# Tencent Hunyuan DiT Release Date: 14 May 2024
+# THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
+# By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+# 1.	DEFINITIONS.
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+# b.	“Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of Tencent Hunyuan Works or any portion or element thereof set forth herein.
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+# d.	“Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
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+# 4.	ADDITIONAL COMMERCIAL TERMS.
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+# 5.	RULES OF USE.
+# a.	Your use of the Tencent Hunyuan Works must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Tencent Hunyuan Works, which is hereby incorporated by reference into this Agreement. You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan Works are subject to the use restrictions in these Sections 5(a) and 5(b).
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+#
+# EXHIBIT A
+# ACCEPTABLE USE POLICY
+
+# Tencent reserves the right to update this Acceptable Use Policy from time to time.
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+
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+
+# ---- End of Tencent Hunyuan Community License Agreement ----
+
+# Please note that the use of this code is subject to the terms and conditions
+# of the Tencent Hunyuan Community License Agreement, including the Acceptable Use Policy.
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention, AttentionProcessor
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import (
+    AdaLayerNormContinuous,
+    FP32LayerNorm,
+    LayerNorm,
+)
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_version,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from torch import nn
+
+from ..attention_processor import FusedTripoSGAttnProcessor2_0, TripoSGAttnProcessor2_0
+from .modeling_outputs import Transformer1DModelOutput
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@maybe_allow_in_graph
+class DiTBlock(nn.Module):
+    r"""
+    Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and
+    QKNorm
+
+    Parameters:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of headsto use for multi-head attention.
+        cross_attention_dim (`int`,*optional*):
+            The size of the encoder_hidden_states vector for cross attention.
+        dropout(`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        activation_fn (`str`,*optional*, defaults to `"geglu"`):
+            Activation function to be used in feed-forward. .
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_eps (`float`, *optional*, defaults to 1e-6):
+            A small constant added to the denominator in normalization layers to prevent division by zero.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*):
+            The size of the hidden layer in the feed-forward block. Defaults to `None`.
+        ff_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the feed-forward block.
+        skip (`bool`, *optional*, defaults to `False`):
+            Whether to use skip connection. Defaults to `False` for down-blocks and mid-blocks.
+        qk_norm (`bool`, *optional*, defaults to `True`):
+            Whether to use normalization in QK calculation. Defaults to `True`.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        use_self_attention: bool = True,
+        self_attention_norm_type: Optional[str] = None, 
+        use_cross_attention: bool = True, # ada layer norm
+        cross_attention_dim: Optional[int] = None,
+        cross_attention_norm_type: Optional[str] = "fp32_layer_norm",
+        use_cross_attention_2: bool = False,
+        cross_attention_2_dim: Optional[int] = None,
+        cross_attention_2_norm_type: Optional[str] = None,
+        dropout=0.0,
+        activation_fn: str = "gelu",
+        norm_type: str = "fp32_layer_norm",  # TODO
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        final_dropout: bool = False,
+        ff_inner_dim: Optional[int] = None,  # int(dim * 4) if None
+        ff_bias: bool = True,
+        skip: bool = False,
+        skip_concat_front: bool = False,  # [x, skip] or [skip, x]
+        skip_norm_last: bool = False,  # this is an error
+        qk_norm: bool = True,
+        qkv_bias: bool = True,
+    ):
+        super().__init__()
+
+        self.use_self_attention = use_self_attention
+        self.use_cross_attention = use_cross_attention
+        self.use_cross_attention_2 = use_cross_attention_2
+        self.skip_concat_front = skip_concat_front
+        self.skip_norm_last = skip_norm_last
+        # Define 3 blocks. Each block has its own normalization layer.
+        # NOTE: when new version comes, check norm2 and norm 3
+        # 1. Self-Attn
+        if use_self_attention:
+            if (
+                self_attention_norm_type == "fp32_layer_norm"
+                or self_attention_norm_type is None
+            ):
+                self.norm1 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+            else:
+                raise NotImplementedError
+
+            self.attn1 = Attention(
+                query_dim=dim,
+                cross_attention_dim=None,
+                dim_head=dim // num_attention_heads,
+                heads=num_attention_heads,
+                qk_norm="rms_norm" if qk_norm else None,
+                eps=1e-6,
+                bias=qkv_bias,
+                processor=TripoSGAttnProcessor2_0(),
+            )
+
+        # 2. Cross-Attn
+        if use_cross_attention:
+            assert cross_attention_dim is not None
+
+            self.norm2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                dim_head=dim // num_attention_heads,
+                heads=num_attention_heads,
+                qk_norm="rms_norm" if qk_norm else None,
+                cross_attention_norm=cross_attention_norm_type,
+                eps=1e-6,
+                bias=qkv_bias,
+                processor=TripoSGAttnProcessor2_0(),
+            )
+
+        if use_cross_attention_2:
+            assert cross_attention_2_dim is not None
+
+            self.norm2_2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+            self.attn2_2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_2_dim,
+                dim_head=dim // num_attention_heads,
+                heads=num_attention_heads,
+                qk_norm="rms_norm" if qk_norm else None,
+                cross_attention_norm=cross_attention_2_norm_type,
+                eps=1e-6,
+                bias=qkv_bias,
+                processor=TripoSGAttnProcessor2_0(),
+            )
+
+        # 3. Feed-forward
+        self.norm3 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,  ### 0.0
+            activation_fn=activation_fn,  ### approx GeLU
+            final_dropout=final_dropout,  ### 0.0
+            inner_dim=ff_inner_dim,  ### int(dim * mlp_ratio)
+            bias=ff_bias,
+        )
+
+        # 4. Skip Connection
+        if skip:
+            self.skip_norm = FP32LayerNorm(dim, norm_eps, elementwise_affine=True)
+            self.skip_linear = nn.Linear(2 * dim, dim)
+        else:
+            self.skip_linear = None
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_topk(self, topk):
+        self.flash_processor.topk = topk
+
+    def set_flash_processor(self, flash_processor):
+        self.flash_processor = flash_processor
+        self.attn2.processor = self.flash_processor
+
+    # Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        skip: Optional[torch.Tensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> torch.Tensor:
+        # Prepare attention kwargs
+        attention_kwargs = attention_kwargs.copy() if attention_kwargs is not None else {}
+        cross_attention_scale = attention_kwargs.pop("cross_attention_scale", 1.0)
+        cross_attention_2_scale = attention_kwargs.pop("cross_attention_2_scale", 1.0)
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Long Skip Connection
+        if self.skip_linear is not None:
+            cat = torch.cat(
+                (
+                    [skip, hidden_states]
+                    if self.skip_concat_front
+                    else [hidden_states, skip]
+                ),
+                dim=-1,
+            )
+            if self.skip_norm_last:
+                # don't do this
+                hidden_states = self.skip_linear(cat)
+                hidden_states = self.skip_norm(hidden_states)
+            else:
+                cat = self.skip_norm(cat)
+                hidden_states = self.skip_linear(cat)
+
+        # 1. Self-Attention
+        if self.use_self_attention:
+            norm_hidden_states = self.norm1(hidden_states)
+            attn_output = self.attn1(
+                norm_hidden_states,
+                image_rotary_emb=image_rotary_emb,
+                **attention_kwargs,
+            )
+            hidden_states = hidden_states + attn_output
+
+        # 2. Cross-Attention
+        if self.use_cross_attention:
+            if self.use_cross_attention_2:
+                hidden_states = (
+                    hidden_states
+                    + self.attn2(
+                        self.norm2(hidden_states),
+                        encoder_hidden_states=encoder_hidden_states,
+                        image_rotary_emb=image_rotary_emb,
+                        **attention_kwargs,
+                    ) * cross_attention_scale
+                    + self.attn2_2(
+                        self.norm2_2(hidden_states),
+                        encoder_hidden_states=encoder_hidden_states_2,
+                        image_rotary_emb=image_rotary_emb,
+                        **attention_kwargs,
+                    ) * cross_attention_2_scale
+                )
+            else:
+                hidden_states = hidden_states + self.attn2(
+                    self.norm2(hidden_states),
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_rotary_emb=image_rotary_emb,
+                    **attention_kwargs,
+                ) * cross_attention_scale
+
+        # FFN Layer ### TODO: switch norm2 and norm3 in the state dict
+        mlp_inputs = self.norm3(hidden_states)
+        hidden_states = hidden_states + self.ff(mlp_inputs)
+
+        return hidden_states
+
+
+class TripoSGDiTModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    """
+    TripoSG: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88):
+            The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        patch_size (`int`, *optional*):
+            The size of the patch to use for the input.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`):
+            Activation function to use in feed-forward.
+        sample_size (`int`, *optional*):
+            The width of the latent images. This is fixed during training since it is used to learn a number of
+            position embeddings.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        cross_attention_dim (`int`, *optional*):
+            The number of dimension in the clip text embedding.
+        hidden_size (`int`, *optional*):
+            The size of hidden layer in the conditioning embedding layers.
+        num_layers (`int`, *optional*, defaults to 1):
+            The number of layers of Transformer blocks to use.
+        mlp_ratio (`float`, *optional*, defaults to 4.0):
+            The ratio of the hidden layer size to the input size.
+        learn_sigma (`bool`, *optional*, defaults to `True`):
+             Whether to predict variance.
+        cross_attention_dim_t5 (`int`, *optional*):
+            The number dimensions in t5 text embedding.
+        pooled_projection_dim (`int`, *optional*):
+            The size of the pooled projection.
+        text_len (`int`, *optional*):
+            The length of the clip text embedding.
+        text_len_t5 (`int`, *optional*):
+            The length of the T5 text embedding.
+        use_style_cond_and_image_meta_size (`bool`,  *optional*):
+            Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        width: int = 2048,
+        in_channels: int = 64,
+        num_layers: int = 21,
+        cross_attention_dim: int = 1024,
+        use_cross_attention_2: bool = False,
+        cross_attention_2_dim: Optional[int] = None
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.num_heads = num_attention_heads
+        self.inner_dim = width
+        self.mlp_ratio = 4.0
+
+        time_embed_dim, timestep_input_dim = self._set_time_proj(
+            "positional",
+            inner_dim=self.inner_dim,
+            flip_sin_to_cos=False,
+            freq_shift=0,
+            time_embedding_dim=None,
+        )
+        self.time_proj = TimestepEmbedding(
+            timestep_input_dim, time_embed_dim, act_fn="gelu", out_dim=self.inner_dim
+        )
+        self.proj_in = nn.Linear(self.config.in_channels, self.inner_dim, bias=True)
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    use_self_attention=True,
+                    self_attention_norm_type="fp32_layer_norm",
+                    use_cross_attention=True,
+                    cross_attention_dim=cross_attention_dim,
+                    cross_attention_norm_type=None,
+                    use_cross_attention_2=use_cross_attention_2,
+                    cross_attention_2_dim=cross_attention_2_dim,
+                    cross_attention_2_norm_type=None,
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",  # TODO
+                    norm_eps=1e-5,
+                    ff_inner_dim=int(self.inner_dim * self.mlp_ratio),
+                    skip=layer > num_layers // 2,
+                    skip_concat_front=True,
+                    skip_norm_last=True,  # this is an error
+                    qk_norm=True,  # See http://arxiv.org/abs/2302.05442 for details.
+                    qkv_bias=False,
+                )
+                for layer in range(num_layers)
+            ]
+        )
+
+        self.norm_out = LayerNorm(self.inner_dim)
+        self.proj_out = nn.Linear(self.inner_dim, self.out_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        self.gradient_checkpointing = value
+
+    def _set_time_proj(
+        self,
+        time_embedding_type: str,
+        inner_dim: int,
+        flip_sin_to_cos: bool,
+        freq_shift: float,
+        time_embedding_dim: int,
+    ) -> Tuple[int, int]:
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or inner_dim * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(
+                    f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}."
+                )
+            self.time_embed = GaussianFourierProjection(
+                time_embed_dim // 2,
+                set_W_to_weight=False,
+                log=False,
+                flip_sin_to_cos=flip_sin_to_cos,
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or inner_dim * 4
+
+            self.time_embed = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
+            timestep_input_dim = inner_dim
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        return time_embed_dim, timestep_input_dim
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedTripoSGAttnProcessor2_0
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError(
+                    "`fuse_qkv_projections()` is not supported for models having added KV projections."
+                )
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+        self.set_attn_processor(FusedTripoSGAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(TripoSGAttnProcessor2_0())
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        timestep: Union[int, float, torch.LongTensor],
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ):
+        """
+        The [`HunyuanDiT2DModel`] forward method.
+
+        Args:
+        hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
+            The input tensor.
+        timestep ( `torch.LongTensor`, *optional*):
+            Used to indicate denoising step.
+        encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+            Conditional embeddings for cross attention layer.
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if (
+                attention_kwargs is not None
+                and attention_kwargs.get("scale", None) is not None
+            ):
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        _, N, _ = hidden_states.shape
+
+        temb = self.time_embed(timestep).to(hidden_states.dtype)
+        temb = self.time_proj(temb)
+        temb = temb.unsqueeze(dim=1)  # unsqueeze to concat with hidden_states
+
+        hidden_states = self.proj_in(hidden_states)
+
+        # N + 1 token
+        hidden_states = torch.cat([temb, hidden_states], dim=1)
+
+        skips = []
+        for layer, block in enumerate(self.blocks):
+            skip = None if layer <= self.config.num_layers // 2 else skips.pop()
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_hidden_states_2,
+                    temb,
+                    image_rotary_emb,
+                    skip,
+                    attention_kwargs,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_hidden_states_2=encoder_hidden_states_2,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    skip=skip,
+                    attention_kwargs=attention_kwargs,
+                )  # (N, L, D)
+
+            if layer < self.config.num_layers // 2:
+                skips.append(hidden_states)
+
+        # final layer
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = hidden_states[:, -N:]
+        hidden_states = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (hidden_states,)
+
+        return Transformer1DModelOutput(sample=hidden_states)
+
+    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
+    def enable_forward_chunking(
+        self, chunk_size: Optional[int] = None, dim: int = 0
+    ) -> None:
+        """
+        Sets the attention processor to use [feed forward
+        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
+
+        Parameters:
+            chunk_size (`int`, *optional*):
+                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
+                over each tensor of dim=`dim`.
+            dim (`int`, *optional*, defaults to `0`):
+                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
+                or dim=1 (sequence length).
+        """
+        if dim not in [0, 1]:
+            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
+
+        # By default chunk size is 1
+        chunk_size = chunk_size or 1
+
+        def fn_recursive_feed_forward(
+            module: torch.nn.Module, chunk_size: int, dim: int
+        ):
+            if hasattr(module, "set_chunk_feed_forward"):
+                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+            for child in module.children():
+                fn_recursive_feed_forward(child, chunk_size, dim)
+
+        for module in self.children():
+            fn_recursive_feed_forward(module, chunk_size, dim)
+
+    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
+    def disable_forward_chunking(self):
+        def fn_recursive_feed_forward(
+            module: torch.nn.Module, chunk_size: int, dim: int
+        ):
+            if hasattr(module, "set_chunk_feed_forward"):
+                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+            for child in module.children():
+                fn_recursive_feed_forward(child, chunk_size, dim)
+
+        for module in self.children():
+            fn_recursive_feed_forward(module, None, 0)
diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py
new file mode 100644
index 0000000..9cd8ebe
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg.py
@@ -0,0 +1,329 @@
+import inspect
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import comfy.utils
+
+import numpy as np
+import PIL
+import PIL.Image
+import torch
+import trimesh
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler  
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    BitImageProcessor,
+    Dinov2Model,
+)
+from ..inference_utils import hierarchical_extract_geometry, flash_extract_geometry
+
+from ..models.autoencoders import TripoSGVAEModel
+from ..models.transformers import TripoSGDiTModel
+from .pipeline_triposg_output import TripoSGPipelineOutput
+from .pipeline_utils import TransformerDiffusionMixin
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class TripoSGPipeline(DiffusionPipeline, TransformerDiffusionMixin):
+    """
+    Pipeline for image-to-3D generation.        
+    """
+
+    def __init__(
+        self,   
+        vae: TripoSGVAEModel,
+        transformer: TripoSGDiTModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        image_encoder_dinov2: Dinov2Model,
+        feature_extractor_dinov2: BitImageProcessor,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+            image_encoder_dinov2=image_encoder_dinov2,
+            feature_extractor_dinov2=feature_extractor_dinov2,
+        )
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def decode_progressive(self):
+        return self._decode_progressive
+
+    def encode_image(self, image, device, num_images_per_prompt):
+        dtype = next(self.image_encoder_dinov2.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor_dinov2(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        image_embeds = self.image_encoder_dinov2(image).last_hidden_state
+        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+        uncond_image_embeds = torch.zeros_like(image_embeds)
+
+        return image_embeds, uncond_image_embeds
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_tokens,
+        num_channels_latents,
+        dtype,
+        device,
+        generator,
+        latents: Optional[torch.Tensor] = None,
+    ):
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        shape = (batch_size, num_tokens, num_channels_latents)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        num_inference_steps: int = 50,
+        num_tokens: int = 2048,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_shapes_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        bounds: Union[Tuple[float], List[float], float] = (-1.005, -1.005, -1.005, 1.005, 1.005, 1.005),
+        dense_octree_depth: int = 8, 
+        hierarchical_octree_depth: int = 9,
+        flash_octree_depth: int = 9,
+        use_flash_decoder: bool = True,
+        return_dict: bool = True,
+    ):
+        # 1. Define call parameters
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            batch_size = image.shape[0]
+        else:
+            raise ValueError("Invalid input type for image")
+
+        device = self._execution_device
+
+        # 3. Encode condition
+        image_embeds, negative_image_embeds = self.encode_image(
+            image, device, num_shapes_per_prompt
+        )
+
+        if self.do_classifier_free_guidance:
+            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps
+        )
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_shapes_per_prompt,
+            num_tokens,
+            num_channels_latents,
+            image_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Denoising loop
+        comfy_pbar = comfy.utils.ProgressBar(num_inference_steps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance
+                    else latents
+                )
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    timestep,
+                    encoder_hidden_states=image_embeds,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_image = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (
+                        noise_pred_image - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, return_dict=False
+                )[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    
+                comfy_pbar.update_absolute(i + 1)
+
+
+        # 7. decoder mesh
+        if not use_flash_decoder:
+            geometric_func = lambda x: self.vae.decode(latents, sampled_points=x).sample
+            output = hierarchical_extract_geometry(
+                geometric_func,
+                device,
+                bounds=bounds,
+                dense_octree_depth=dense_octree_depth,
+                hierarchical_octree_depth=hierarchical_octree_depth,
+            )
+        else:
+            self.vae.set_flash_decoder()
+            output = flash_extract_geometry(
+                latents,
+                self.vae,
+                bounds=bounds,
+                octree_depth=flash_octree_depth,
+            )
+        meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output]
+        
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output, meshes)
+
+        return TripoSGPipelineOutput(samples=output, meshes=meshes)
+
diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py
new file mode 100644
index 0000000..bc4c7e0
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_output.py
@@ -0,0 +1,17 @@
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import trimesh
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class TripoSGPipelineOutput(BaseOutput):
+    r"""
+    Output class for ShapeDiff pipelines.
+    """
+
+    samples: torch.Tensor
+    meshes: List[trimesh.Trimesh]
diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py
new file mode 100644
index 0000000..9acf745
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_triposg_scribble.py
@@ -0,0 +1,343 @@
+import inspect
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import comfy.utils
+
+import numpy as np
+import PIL
+import PIL.Image
+import torch
+import trimesh
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler  # not sure
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    BitImageProcessor,
+    CLIPTokenizer,
+    CLIPTextModelWithProjection,
+    Dinov2Model,
+)
+from ..inference_utils import hierarchical_extract_geometry, flash_extract_geometry
+
+from ..models.autoencoders import TripoSGVAEModel
+from ..models.transformers import TripoSGDiTModel
+from .pipeline_triposg_output import TripoSGPipelineOutput
+from .pipeline_utils import TransformerDiffusionMixin
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class TripoSGScribblePipeline(DiffusionPipeline, TransformerDiffusionMixin):
+    """
+    Pipeline for (scribble and text)-to-3D generation.   
+    """
+
+    def __init__(
+        self,
+        vae: TripoSGVAEModel,
+        transformer: TripoSGDiTModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        tokenizer: CLIPTokenizer,
+        text_encoder: CLIPTextModelWithProjection,
+        image_encoder_dinov2: Dinov2Model,
+        feature_extractor_dinov2: BitImageProcessor,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            image_encoder_dinov2=image_encoder_dinov2,
+            feature_extractor_dinov2=feature_extractor_dinov2
+        )
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+    
+    def encode_text(self, prompt, device, num_shapes_per_prompt):
+        input_ids = self.tokenizer(
+            [prompt], max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )['input_ids'].to(device)
+        text_embeds = self.text_encoder(input_ids).last_hidden_state
+        text_embeds = text_embeds.repeat_interleave(num_shapes_per_prompt, dim=0)
+        uncond_text_embeds = torch.zeros_like(text_embeds)
+        return text_embeds, uncond_text_embeds
+
+    def encode_image(self, image, device, num_shapes_per_prompt):
+        dtype = next(self.image_encoder_dinov2.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor_dinov2(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        image_embeds = self.image_encoder_dinov2(image).last_hidden_state
+        image_embeds = image_embeds.repeat_interleave(num_shapes_per_prompt, dim=0)
+        uncond_image_embeds = torch.zeros_like(image_embeds)
+
+        return image_embeds, uncond_image_embeds
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_tokens,
+        num_channels_latents,
+        dtype,
+        device,
+        generator,
+        latents: Optional[torch.Tensor] = None,
+    ):
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+        
+        shape = (batch_size, num_tokens, num_channels_latents)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        prompt: str,
+        num_tokens: int = 512,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_shapes_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        bounds: Union[Tuple[float], List[float], float] = (-1.005, -1.005, -1.005, 1.005, 1.005, 1.005),
+        dense_octree_depth: int = 8, 
+        hierarchical_octree_depth: int = 9,
+        flash_octree_depth: int = 9,
+        use_flash_decoder: bool = True,
+        return_dict: bool = True,
+    ):
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            batch_size = image.shape[0]
+        else:
+            raise ValueError("Invalid input type for image")
+
+        device = self._execution_device
+
+        # 3. Encode condition
+        text_embeds, negative_text_embeds = self.encode_text(
+            prompt, device, num_shapes_per_prompt
+        )
+
+        image_embeds, negative_image_embeds = self.encode_image(
+            image, device, num_shapes_per_prompt
+        )        
+
+        if self.do_classifier_free_guidance:
+            text_embeds = torch.cat([negative_text_embeds, text_embeds], dim=0)
+            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps
+        )
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_shapes_per_prompt,
+            num_tokens,
+            num_channels_latents,
+            image_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Denoising loop
+        comfy_pbar = comfy.utils.ProgressBar(num_inference_steps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance
+                    else latents
+                )
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    timestep.to(dtype=latents.dtype),
+                    encoder_hidden_states=text_embeds,
+                    encoder_hidden_states_2=image_embeds,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_image = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (
+                        noise_pred_image - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, return_dict=False
+                )[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    
+                comfy_pbar.update_absolute(i + 1)
+
+        # 7. decoder mesh
+        if not use_flash_decoder:
+            geometric_func = lambda x: self.vae.decode(latents, sampled_points=x).sample
+            output = hierarchical_extract_geometry(
+                geometric_func,
+                device,
+                bounds=bounds,
+                dense_octree_depth=dense_octree_depth,
+                hierarchical_octree_depth=hierarchical_octree_depth,
+            )
+        else:
+            self.vae.set_flash_decoder()
+            output = flash_extract_geometry(
+                latents,
+                self.vae,
+                bounds=bounds,
+                octree_depth=flash_octree_depth,
+            )
+        meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output]
+        
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output, meshes)
+
+        return TripoSGPipelineOutput(samples=output, meshes=meshes)
diff --git a/Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py b/Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py
new file mode 100644
index 0000000..a2dc441
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/pipelines/pipeline_utils.py
@@ -0,0 +1,96 @@
+from diffusers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class TransformerDiffusionMixin:
+    r"""
+    Helper for DiffusionPipeline with vae and transformer.(mainly for DIT)
+    """
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    def fuse_qkv_projections(self, transformer: bool = True, vae: bool = True):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        Args:
+            transformer (`bool`, defaults to `True`): To apply fusion on the Transformer.
+            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+        """
+        self.fusing_transformer = False
+        self.fusing_vae = False
+
+        if transformer:
+            self.fusing_transformer = True
+            self.transformer.fuse_qkv_projections()
+
+        if vae:
+            self.fusing_vae = True
+            self.vae.fuse_qkv_projections()
+
+    def unfuse_qkv_projections(self, transformer: bool = True, vae: bool = True):
+        """Disable QKV projection fusion if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        Args:
+            transformer (`bool`, defaults to `True`): To apply fusion on the Transformer.
+            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+
+        """
+        if transformer:
+            if not self.fusing_transformer:
+                logger.warning(
+                    "The UNet was not initially fused for QKV projections. Doing nothing."
+                )
+            else:
+                self.transformer.unfuse_qkv_projections()
+                self.fusing_transformer = False
+
+        if vae:
+            if not self.fusing_vae:
+                logger.warning(
+                    "The VAE was not initially fused for QKV projections. Doing nothing."
+                )
+            else:
+                self.vae.unfuse_qkv_projections()
+                self.fusing_vae = False
diff --git a/Gen_3D_Modules/TripoSG/schedulers/__init__.py b/Gen_3D_Modules/TripoSG/schedulers/__init__.py
new file mode 100644
index 0000000..619c331
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/schedulers/__init__.py
@@ -0,0 +1,5 @@
+from .scheduling_rectified_flow import (
+    RectifiedFlowScheduler,
+    compute_density_for_timestep_sampling,
+    compute_loss_weighting,
+)
diff --git a/Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py b/Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py
new file mode 100644
index 0000000..d97c1bb
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/schedulers/scheduling_rectified_flow.py
@@ -0,0 +1,327 @@
+"""
+Adapted from https://github.com/huggingface/diffusers/blob/v0.30.3/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py.
+"""
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import BaseOutput, logging
+from torch.distributions import LogisticNormal
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# TODO: may move to training_utils.py
+def compute_density_for_timestep_sampling(
+    weighting_scheme: str,
+    batch_size: int,
+    logit_mean: float = 0.0,
+    logit_std: float = 1.0,
+    mode_scale: float = None,
+):
+    if weighting_scheme == "logit_normal":
+        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
+        u = torch.normal(
+            mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu"
+        )
+        u = torch.nn.functional.sigmoid(u)
+    elif weighting_scheme == "logit_normal_dist":
+        u = (
+            LogisticNormal(loc=logit_mean, scale=logit_std)
+            .sample((batch_size,))[:, 0]
+            .to("cpu")
+        )
+    elif weighting_scheme == "mode":
+        u = torch.rand(size=(batch_size,), device="cpu")
+        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
+    else:
+        u = torch.rand(size=(batch_size,), device="cpu")
+    return u
+
+
+def compute_loss_weighting(weighting_scheme: str, sigmas=None):
+    """
+    Computes loss weighting scheme for SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "sigma_sqrt":
+        weighting = (sigmas**-2.0).float()
+    elif weighting_scheme == "cosmap":
+        bot = 1 - 2 * sigmas + 2 * sigmas**2
+        weighting = 2 / (math.pi * bot)
+    else:
+        weighting = torch.ones_like(sigmas)
+    return weighting
+
+
+@dataclass
+class RectifiedFlowSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+    """
+
+    prev_sample: torch.FloatTensor
+
+
+class RectifiedFlowScheduler(SchedulerMixin, ConfigMixin):
+    """
+    The rectified flow scheduler is a scheduler that is used to propagate the diffusion process in the rectified flow.
+
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        timestep_spacing (`str`, defaults to `"linspace"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
+        shift (`float`, defaults to 1.0):
+            The shift value for the timestep schedule.
+    """
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        shift: float = 1.0,
+        use_dynamic_shifting: bool = False,
+    ):
+        # pre-compute timesteps and sigmas; no use in fact
+        # NOTE that shape diffusion sample timesteps randomly or in a distribution,
+        # instead of sampling from the pre-defined linspace
+        timesteps = np.array(
+            [
+                (1.0 - i / num_train_timesteps) * num_train_timesteps
+                for i in range(num_train_timesteps)
+            ]
+        )
+        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+
+        sigmas = timesteps / num_train_timesteps
+        if not use_dynamic_shifting:
+            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
+            sigmas = self.time_shift(sigmas)
+
+        self.timesteps = sigmas * num_train_timesteps
+
+        self._step_index = None
+        self._begin_index = None
+
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def _sigma_to_t(self, sigma):
+        return sigma * self.config.num_train_timesteps
+
+    def _t_to_sigma(self, timestep):
+        return timestep / self.config.num_train_timesteps
+
+    def time_shift_dynamic(self, mu: float, sigma: float, t: torch.Tensor):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    def time_shift(self, t: torch.Tensor):
+        return self.config.shift * t / (1 + (self.config.shift - 1) * t)
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError(
+                " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`"
+            )
+
+        if sigmas is None:
+            self.num_inference_steps = num_inference_steps
+            timesteps = np.array(
+                [
+                    (1.0 - i / num_inference_steps) * self.config.num_train_timesteps
+                    for i in range(num_inference_steps)
+                ]
+            )  # different from the original code in SD3
+            sigmas = timesteps / self.config.num_train_timesteps
+
+        if self.config.use_dynamic_shifting:
+            sigmas = self.time_shift_dynamic(mu, 1.0, sigmas)
+        else:
+            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
+
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
+        timesteps = sigmas * self.config.num_train_timesteps
+
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        s_churn: float = 0.0,
+        s_tmin: float = 0.0,
+        s_tmax: float = float("inf"),
+        s_noise: float = 1.0,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[RectifiedFlowSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            s_churn (`float`):
+            s_tmin  (`float`):
+            s_tmax  (`float`):
+            s_noise (`float`, defaults to 1.0):
+                Scaling factor for noise added to the sample.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            return_dict (`bool`):
+                Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
+                tuple.
+
+        Returns:
+            [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
+                returned, otherwise a tuple is returned where the first element is the sample tensor.
+        """
+
+        if (
+            isinstance(timestep, int)
+            or isinstance(timestep, torch.IntTensor)
+            or isinstance(timestep, torch.LongTensor)
+        ):
+            raise ValueError(
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        # Upcast to avoid precision issues when computing prev_sample
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas[self.step_index]
+        sigma_next = self.sigmas[self.step_index + 1]
+
+        # Here different directions are used for the flow matching
+        prev_sample = sample + (sigma - sigma_next) * model_output
+
+        # Cast sample back to model compatible dtype
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return RectifiedFlowSchedulerOutput(prev_sample=prev_sample)
+
+    def scale_noise(
+        self,
+        original_samples: torch.Tensor,
+        noise: torch.Tensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.Tensor:
+        """
+        Forward function for the noise scaling in the flow matching.
+        """
+        sigmas = self._t_to_sigma(timesteps.to(dtype=torch.float32))
+
+        while len(sigmas.shape) < len(original_samples.shape):
+            sigmas = sigmas.unsqueeze(-1)
+
+        return (1.0 - sigmas) * original_samples + sigmas * noise
+
+    def __len__(self):
+        return self.config.num_train_timesteps
diff --git a/Gen_3D_Modules/TripoSG/utils/typing.py b/Gen_3D_Modules/TripoSG/utils/typing.py
new file mode 100644
index 0000000..8a84e73
--- /dev/null
+++ b/Gen_3D_Modules/TripoSG/utils/typing.py
@@ -0,0 +1,64 @@
+"""
+This module contains type annotations for the project, using
+1. Python type hints (https://docs.python.org/3/library/typing.html) for Python objects
+2. jaxtyping (https://github.com/google/jaxtyping/blob/main/API.md) for PyTorch tensors
+
+Two types of typing checking can be used:
+1. Static type checking with mypy (install with pip and enabled as the default linter in VSCode)
+2. Runtime type checking with typeguard (install with pip and triggered at runtime, mainly for tensor dtype and shape checking)
+"""
+
+# Basic types
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Literal,
+    NamedTuple,
+    NewType,
+    Optional,
+    Sized,
+    Tuple,
+    Type,
+    TypedDict,
+    TypeVar,
+    Union,
+)
+
+# Tensor dtype
+# for jaxtyping usage, see https://github.com/google/jaxtyping/blob/main/API.md
+from jaxtyping import Bool, Complex, Float, Inexact, Int, Integer, Num, Shaped, UInt
+
+# Config type
+from omegaconf import DictConfig, ListConfig
+
+# PyTorch Tensor type
+from torch import Tensor
+
+# Runtime type checking decorator
+from typeguard import typechecked as typechecker
+
+
+# Custom types
+class FuncArgs(TypedDict):
+    """Type for instantiating a function with keyword arguments"""
+
+    name: str
+    kwargs: Dict[str, Any]
+
+    @staticmethod
+    def validate(variable):
+        necessary_keys = ["name", "kwargs"]
+        for key in necessary_keys:
+            assert key in variable, f"Key {key} is missing in {variable}"
+        if not isinstance(variable["name"], str):
+            raise TypeError(
+                f"Key 'name' should be a string, not {type(variable['name'])}"
+            )
+        if not isinstance(variable["kwargs"], dict):
+            raise TypeError(
+                f"Key 'kwargs' should be a dictionary, not {type(variable['kwargs'])}"
+            )
+        return variable
diff --git a/README.md b/README.md
index 060445c..c05609b 100644
--- a/README.md
+++ b/README.md
@@ -43,9 +43,46 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py
 
 
 ## Features:
-- For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 22/January/2025**]
+- For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 5/June/2025**]
   - **Note:** you need to put [Example Inputs Files & Folders](_Example_Workflows/_Example_Inputs_Files/) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow
 
+- **MV-Adapter**: [huanngzh/MV-Adapter](https://github.com/huanngzh/MV-Adapter)
+  - Two generation methods:
+    - IG2MV: Single image + 3D mesh to multi-view images
+    - TG2MV: Text prompt + 3D mesh to multi-view images
+    - Texturing: Grid image + 3D mesh to textured mesh
+  - Model weights: https://huggingface.co/huanngzh/mv-adapter
+  - Workflows: [IG2MV](./_Example_Workflows/MV-Adapter/MV-Adapter-ig2mv.json), [T2MV](./_Example_Workflows/MV-Adapter/MV-Adapter-tg2mv.json), [Texturing](./_Example_Workflows/MV-Adapter/MV-Adapter-Texturing.json)
+    
+   <video controls autoplay loop src="https://github.com/user-attachments/assets/47b77c9e-a121-45c4-a6d0-f307bda1579c"></video>
+   <video controls autoplay loop src="https://github.com/user-attachments/assets/35c6cee5-d408-4559-88aa-753741eacb95"></video>
+   <video controls autoplay loop src="https://github.com/user-attachments/assets/dcf33c27-0ff7-4379-91b5-a0efe229d63c"></video>
+   
+- **Stable3DGen**: [Stable-X/Stable3DGen](https://github.com/Stable-X/Stable3DGen)
+  - Two models pipeline:
+    - Stable3DGen: Single image to 3D Mesh
+    - StableNormal: Image processing for normal map generation
+  - Model weights: 
+    - Stable3DGen: https://huggingface.co/Stable-X/trellis-normal-v0-1
+    - StableNormal: https://huggingface.co/Stable-X/yoso-normal-v1-8-1
+  - [Workflow](./_Example_Workflows/Stable3DGen.json)
+    
+  <video controls autoplay loop src="https://github.com/user-attachments/assets/2a38fa0a-4028-4fe5-a67a-bbc7c305da75"></video>
+ 
+- **Hunyuan3D_V2**: turbo, mini, fast, multiview
+  - Single image to 3D Mesh
+  - Multi-views to 3D Mesh with RGB texture
+  - Model weights: https://huggingface.co/tencent/Hunyuan3D-2, https://huggingface.co/tencent/Hunyuan3D-2mini
+  - [Workflows](./_Example_Workflows/Hunyuan3D_V2)
+    
+  <video controls autoplay loop src="https://github.com/user-attachments/assets/ae0f68d8-edd3-4bdd-9a9a-1f5ccc07a3d0"></video>
+
+- **TripoSG**: [VAST-AI-Research/TripoSG](https://github.com/VAST-AI-Research/TripoSG)
+  - Single image (Reference or Scribble) to 3D Mesh
+  - Model weights: https://huggingface.co/VAST-AI/TripoSG, https://huggingface.co/VAST-AI/TripoSG-scribble
+
+  <video controls autoplay loop src="https://github.com/user-attachments/assets/7b7969c1-e7c8-465b-9605-8bba7e847dbf"></video>
+
 - **TRELLIS**: [microsoft/TRELLIS](https://github.com/microsoft/TRELLIS)
   - Single image to 3D Mesh with RGB texture
   - Model weights: https://huggingface.co/jetx/TRELLIS-image-large
@@ -57,6 +94,7 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py
     1. Single image to 3D Mesh shape only
     2. 3D Mesh shape + Single reference image to 3D Mesh with RGB texture
   - Model weights: https://huggingface.co/tencent/Hunyuan3D-2/tree/main
+  - [Workflows](https://github.com/DenisKochetov/ComfyUI-3D-Pack/tree/dev/_Example_Workflows/Hunyuan3D_V2)
 
   <video controls autoplay loop src="https://github.com/user-attachments/assets/da6a8f8b-8dab-4460-90a6-44769ec9bb09"></video>
 
diff --git a/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2.json b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2.json
new file mode 100644
index 0000000..4b7e698
--- /dev/null
+++ b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2.json
@@ -0,0 +1,494 @@
+{
+  "id": "ecf3b5f9-96c2-411d-92cb-d8bd8cebbe0f",
+  "revision": 0,
+  "last_node_id": 89,
+  "last_link_id": 172,
+  "nodes": [
+    {
+      "id": 87,
+      "type": "[Comfy3D] Hunyuan3D V2 Paint Model Turbo MV",
+      "pos": [
+        948.6290893554688,
+        1604.2415771484375
+      ],
+      "size": [
+        300,
+        68.93335723876953
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "hunyuan3d_v2_texgen_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "link": 160
+        },
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "link": 170
+        },
+        {
+          "name": "images",
+          "type": "LIST",
+          "link": 168
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "links": [
+            163
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Hunyuan3D V2 Paint Model Turbo MV"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 89,
+      "type": "[Comfy3D] Multi Background Remover",
+      "pos": [
+        948.6290893554688,
+        1353.7720947265625
+      ],
+      "size": [
+        300,
+        68.93328857421875
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image_front",
+          "type": "IMAGE",
+          "link": 165
+        },
+        {
+          "name": "image_back",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "image_left",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "LIST",
+          "links": [
+            168,
+            169
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Multi Background Remover"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 27,
+      "type": "[Comfy3D] Preview 3DMesh",
+      "pos": [
+        1337.7381591796875,
+        1499.6043701171875
+      ],
+      "size": [
+        600,
+        500
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh_file_path",
+          "type": "STRING",
+          "widget": {
+            "name": "mesh_file_path"
+          },
+          "link": 105
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Preview 3DMesh"
+      },
+      "widgets_values": [
+        "",
+        null
+      ]
+    },
+    {
+      "id": 71,
+      "type": "[Comfy3D] Load Hunyuan3D V2 TexGen Pipeline",
+      "pos": [
+        948.6290893554688,
+        1481.4090576171875
+      ],
+      "size": [
+        300,
+        64.12879180908203
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "texgen_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "links": [
+            160
+          ]
+        }
+      ],
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new file mode 100644
index 0000000..137f43f
--- /dev/null
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diff --git a/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_MV.json b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_MV.json
new file mode 100644
index 0000000..23d378e
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diff --git a/_Example_Workflows/Hunyuan3D_V2.json b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_MV_Fast.json
similarity index 56%
rename from _Example_Workflows/Hunyuan3D_V2.json
rename to _Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_MV_Fast.json
index bbf316e..242c8c8 100644
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+++ b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_MV_Fast.json
@@ -1,346 +1,38 @@
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new file mode 100644
index 0000000..ff38127
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new file mode 100644
index 0000000..d7b40fd
--- /dev/null
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diff --git a/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Mini_Fast.json b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Mini_Fast.json
new file mode 100644
index 0000000..e75c94c
--- /dev/null
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diff --git a/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Mini_Turbo.json b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Mini_Turbo.json
new file mode 100644
index 0000000..4b22a8b
--- /dev/null
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diff --git a/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Turbo.json b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Turbo.json
new file mode 100644
index 0000000..eea1437
--- /dev/null
+++ b/_Example_Workflows/Hunyuan3D_V2/Hunyuan3D_V2_Turbo.json
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new file mode 100644
index 0000000..9c658e3
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new file mode 100644
index 0000000..bed43a9
--- /dev/null
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diff --git a/_Example_Workflows/MV-Adapter/MV-Adapter-tg2mv.json b/_Example_Workflows/MV-Adapter/MV-Adapter-tg2mv.json
new file mode 100644
index 0000000..8430d12
--- /dev/null
+++ b/_Example_Workflows/MV-Adapter/MV-Adapter-tg2mv.json
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+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image_grid",
+          "type": "IMAGE",
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
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+      "mode": 0,
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+          "type": "IMAGE",
+          "link": 3
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+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
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+    {
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+      "type": "PreviewImage",
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+      "mode": 0,
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+      "outputs": [],
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+        "Node name for S&R": "PreviewImage"
+      },
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+          "name": "mvadapter_tg2mv_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "links": [
+            1
+          ]
+        }
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+        "Node name for S&R": "[Comfy3D] Load MVAdapter TG2MV Pipeline"
+      },
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+        "madebyollin/sdxl-vae-fp16-fix",
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+        true,
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+          "type": "DIFFUSERS_PIPE",
+          "link": 1
+        },
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+          "type": "STRING",
+          "widget": {
+            "name": "mesh_path"
+          },
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "multiview_images",
+          "type": "IMAGE",
+          "links": [
+            4,
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] MVAdapter TG2MV"
+      },
+      "widgets_values": [
+        "",
+        "Mater, a rusty and beat-up tow truck from the 2006 Disney/Pixar animated film 'Cars', with a rusty brown exterior, big blue eyes.",
+        "watermark, ugly, deformed, noisy, blurry, low contrast",
+        6,
+        50,
+        7,
+        768,
+        768,
+        192532995061057,
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+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "title": "Mesh Path",
+      "properties": {
+        "Node name for S&R": "PrimitiveString"
+      },
+      "widgets_values": [
+        "output/StableGenTest/mater.glb"
+      ]
+    }
+  ],
+  "links": [
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+      1,
+      1,
+      0,
+      2,
+      0,
+      "DIFFUSERS_PIPE"
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+      "STRING"
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+      "IMAGE"
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+    [
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+    [
+      5,
+      2,
+      0,
+      5,
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+  "groups": [],
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+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/_Example_Workflows/Stable3DGen.json b/_Example_Workflows/Stable3DGen.json
new file mode 100644
index 0000000..33c30c7
--- /dev/null
+++ b/_Example_Workflows/Stable3DGen.json
@@ -0,0 +1,385 @@
+{
+  "id": "e7d35c6d-c1bf-4f60-9b88-7dc8e70a7b70",
+  "revision": 0,
+  "last_node_id": 14,
+  "last_link_id": 13,
+  "nodes": [
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+      "type": "[Comfy3D] Load StableGen StableX Pipeline",
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+      "flags": {},
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+      "mode": 0,
+      "inputs": [],
+      "outputs": [
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+          "name": "stablex_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "links": [
+            1
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Load StableGen StableX Pipeline"
+      },
+      "widgets_values": [
+        "yoso-normal-v1-8-1",
+        true
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+    },
+    {
+      "id": 9,
+      "type": "PreviewImage",
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+      "mode": 0,
+      "inputs": [
+        {
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+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 4,
+      "type": "[Comfy3D] StableGen StableX Process Image",
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+      "inputs": [
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+          "type": "DIFFUSERS_PIPE",
+          "link": 1
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "processed_image",
+          "type": "IMAGE",
+          "links": [
+            8,
+            11
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] StableGen StableX Process Image"
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+      "type": "[Comfy3D] StableGen Trellis Image To 3D",
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+          "link": 13
+        },
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 11
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "links": [
+            12
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] StableGen Trellis Image To 3D"
+      },
+      "widgets_values": [
+        "single",
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+        7.5,
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+      "mode": 0,
+      "inputs": [
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+          "name": "mesh_file_path",
+          "type": "STRING",
+          "widget": {
+            "name": "mesh_file_path"
+          },
+          "link": 7
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Preview 3DMesh"
+      },
+      "widgets_values": [
+        "",
+        null
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+      "mode": 0,
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+      "outputs": [
+        {
+          "name": "save_path",
+          "type": "STRING",
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Save 3D Mesh"
+      },
+      "widgets_values": [
+        "StableGenTest/mesh.glb"
+      ]
+    },
+    {
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+      "type": "[Comfy3D] Load StableGen Trellis Pipeline",
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+          "name": "trellis_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "links": [
+            13
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Load StableGen Trellis Pipeline"
+      },
+      "widgets_values": [
+        "trellis-normal-v0-1",
+        "dinov2_vitl14_reg",
+        true,
+        "xformers",
+        "spconv",
+        "implicit_gemm",
+        true
+      ]
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+    {
+      "id": 5,
+      "type": "LoadImage",
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+      "flags": {},
+      "order": 2,
+      "mode": 0,
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+          "type": "IMAGE",
+          "links": [
+            4
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
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+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "squirrel_girl_1.png",
+        "image"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      4,
+      0,
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+      1,
+      "IMAGE"
+    ],
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+      "STRING"
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+      10,
+      1,
+      "IMAGE"
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+    [
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+    ],
+    [
+      13,
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+      0,
+      10,
+      0,
+      "DIFFUSERS_PIPE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
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+      "offset": [
+        -1983.6539415508826,
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+      "comfy-core": "0.3.35"
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+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/_Example_Workflows/TripoSG_Scribble_Model.json b/_Example_Workflows/TripoSG_Scribble_Model.json
new file mode 100644
index 0000000..58d6a60
--- /dev/null
+++ b/_Example_Workflows/TripoSG_Scribble_Model.json
@@ -0,0 +1 @@
+{"id":"fdd6e668-46c5-44c1-b283-52ab12950c4d","revision":0,"last_node_id":14,"last_link_id":23,"nodes":[{"id":1,"type":"[Comfy3D] Preview 3DMesh","pos":[2625.242431640625,798.520263671875],"size":[600,500],"flags":{},"order":6,"mode":0,"inputs":[{"localized_name":"mesh_file_path","name":"mesh_file_path","type":"STRING","widget":{"name":"mesh_file_path"},"link":1}],"outputs":[],"properties":{"Node name for S&R":"[Comfy3D] Preview 3DMesh"},"widgets_values":["",null]},{"id":10,"type":"PreviewImage","pos":[2181.3193359375,821.8413696289062],"size":[424.22674560546875,495.2666931152344],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":22}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":11,"type":"[Comfy3D] Preview 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\ No newline at end of file
diff --git a/_Example_Workflows/TripoSG_to_Mesh.json b/_Example_Workflows/TripoSG_to_Mesh.json
new file mode 100644
index 0000000..55a393c
--- /dev/null
+++ b/_Example_Workflows/TripoSG_to_Mesh.json
@@ -0,0 +1 @@
+{"id":"74bbccb9-f4dc-425a-ab0d-43fa635cd3b9","revision":0,"last_node_id":19,"last_link_id":28,"nodes":[{"id":15,"type":"[Comfy3D] Preview 3DMesh","pos":[1775.2972412109375,393.68817138671875],"size":[600,500],"flags":{},"order":11,"mode":0,"inputs":[{"localized_name":"mesh_file_path","name":"mesh_file_path","type":"STRING","widget":{"name":"mesh_file_path"},"link":15}],"outputs":[],"properties":{"Node name for S&R":"[Comfy3D] Preview 3DMesh"},"widgets_values":["",null]},{"id":14,"type":"[Comfy3D] Save 3D Mesh","pos":[1774.304931640625,279.5744934082031],"size":[260.8727111816406,58],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"mesh","name":"mesh","type":"MESH","link":14},{"localized_name":"save_path","name":"save_path","type":"STRING","widget":{"name":"save_path"},"link":null}],"outputs":[{"localized_name":"save_path","name":"save_path","type":"STRING","links":[15]}],"properties":{"Node name for S&R":"[Comfy3D] Save 3D Mesh"},"widgets_values":["TripoSG_Test/mesh_0.glb"]},{"id":13,"type":"[Comfy3D] Save 3D Mesh","pos":[2133.0234375,117.99606323242188],"size":[260.8727111816406,58],"flags":{},"order":10,"mode":0,"inputs":[{"localized_name":"mesh","name":"mesh","type":"MESH","link":12},{"localized_name":"save_path","name":"save_path","type":"STRING","widget":{"name":"save_path"},"link":null}],"outputs":[{"localized_name":"save_path","name":"save_path","type":"STRING","links":[13]}],"properties":{"Node name for S&R":"[Comfy3D] Save 3D Mesh"},"widgets_values":["TripoSG_Test/mesh_1.glb"]},{"id":10,"type":"[Comfy3D] TripoSG I23D Model","pos":[1336.7540283203125,118.28916931152344],"size":[380.4000244140625,246],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"tsg_pipe","name":"tsg_pipe","type":"DIFFUSERS_PIPE","link":9},{"localized_name":"reference_image","name":"reference_image","type":"IMAGE","link":27},{"localized_name":"seed","name":"seed","type":"INT","widget":{"name":"seed"},"link":null},{"localized_name":"guidance_scale","name":"guidance_scale","type":"FLOAT","widget":{"name":"guidance_scale"},"link":null},{"localized_name":"num_inference_steps","name":"num_inference_steps","type":"INT","widget":{"name":"num_inference_steps"},"link":null},{"localized_name":"use_flash_decoder","name":"use_flash_decoder","type":"BOOLEAN","widget":{"name":"use_flash_decoder"},"link":null},{"localized_name":"flash_octree_depth","name":"flash_octree_depth","type":"INT","widget":{"name":"flash_octree_depth"},"link":null},{"localized_name":"hierarchical_octree_depth","name":"hierarchical_octree_depth","type":"INT","widget":{"name":"hierarchical_octree_depth"},"link":null},{"localized_name":"dense_octree_depth","name":"dense_octree_depth","type":"INT","widget":{"name":"dense_octree_depth"},"link":null}],"outputs":[{"localized_name":"mesh","name":"mesh","type":"MESH","links":[11,14]}],"properties":{"Node name for S&R":"[Comfy3D] TripoSG I23D Model"},"widgets_values":[42,"fixed",7,50,true,9,9,8]},{"id":11,"type":"[Comfy3D] Decimate Mesh","pos":[1774.988525390625,118.2227554321289],"size":[315,106],"flags":{},"order":8,"mode":0,"inputs":[{"localized_name":"mesh","name":"mesh","type":"MESH","link":11},{"localized_name":"target","name":"target","type":"INT","widget":{"name":"target"},"link":null},{"localized_name":"remesh","name":"remesh","type":"BOOLEAN","widget":{"name":"remesh"},"link":null},{"localized_name":"optimalplacement","name":"optimalplacement","type":"BOOLEAN","widget":{"name":"optimalplacement"},"link":null}],"outputs":[{"localized_name":"mesh","name":"mesh","type":"MESH","links":[12]}],"properties":{"Node name for S&R":"[Comfy3D] Decimate Mesh"},"widgets_values":[20000,false,true]},{"id":4,"type":"[Comfy3D] Image Add Pure Color Background","pos":[1030.196044921875,260.958251953125],"size":[260.3999938964844,126],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":25},{"localized_name":"masks","name":"masks","type":"MASK","link":26},{"localized_name":"R","name":"R","type":"INT","widget":{"name":"R"},"link":null},{"localized_name":"G","name":"G","type":"INT","widget":{"name":"G"},"link":null},{"localized_name":"B","name":"B","type":"INT","widget":{"name":"B"},"link":null}],"outputs":[{"localized_name":"images","name":"images","type":"IMAGE","slot_index":0,"links":[17,27]}],"properties":{"Node name for S&R":"[Comfy3D] Image Add Pure Color Background"},"widgets_values":[255,255,255]},{"id":18,"type":"[Comfy3D] Resize Image Foreground","pos":[779.76171875,261.325927734375],"size":[210,78],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":22},{"localized_name":"masks","name":"masks","type":"MASK","link":24},{"localized_name":"foreground_ratio","name":"foreground_ratio","type":"FLOAT","widget":{"name":"foreground_ratio"},"link":null}],"outputs":[{"localized_name":"images","name":"images","type":"IMAGE","links":[25,28]},{"localized_name":"masks","name":"masks","type":"MASK","links":[26]}],"properties":{"Node name for S&R":"[Comfy3D] Resize Image Foreground"},"widgets_values":[0.85]},{"id":1,"type":"InvertMask","pos":[619.742919921875,323.9401550292969],"size":[140,26],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"mask","name":"mask","type":"MASK","link":1}],"outputs":[{"localized_name":"MASK","name":"MASK","type":"MASK","slot_index":0,"links":[24]}],"properties":{"Node name for S&R":"InvertMask"},"widgets_values":[]},{"id":2,"type":"LoadImage","pos":[118.70645904541016,262.0327453613281],"size":[473.3881530761719,545.7767944335938],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","slot_index":0,"links":[22]},{"localized_name":"MASK","name":"MASK","type":"MASK","slot_index":1,"links":[1]}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["White Tiger Girl_0.png","image"]},{"id":17,"type":"PreviewImage","pos":[1331.374267578125,417.0093078613281],"size":[424.22674560546875,495.2666931152344],"flags":{},"order":6,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":17}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":12,"type":"[Comfy3D] Preview 3DMesh","pos":[2400.22998046875,393.1309509277344],"size":[600,500],"flags":{},"order":12,"mode":0,"inputs":[{"localized_name":"mesh_file_path","name":"mesh_file_path","type":"STRING","widget":{"name":"mesh_file_path"},"link":13}],"outputs":[],"properties":{"Node name for S&R":"[Comfy3D] Preview 3DMesh"},"widgets_values":["",null]},{"id":5,"type":"[Comfy3D] Load Diffusers Pipeline","pos":[981.5452270507812,-0.8751727938652039],"size":[309.2200012207031,178],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"diffusers_pipeline_name","name":"diffusers_pipeline_name","type":"COMBO","widget":{"name":"diffusers_pipeline_name"},"link":null},{"localized_name":"repo_id","name":"repo_id","type":"STRING","widget":{"name":"repo_id"},"link":null},{"localized_name":"custom_pipeline","name":"custom_pipeline","type":"STRING","widget":{"name":"custom_pipeline"},"link":null},{"localized_name":"force_download","name":"force_download","type":"BOOLEAN","widget":{"name":"force_download"},"link":null},{"localized_name":"checkpoint_sub_dir","name":"checkpoint_sub_dir","shape":7,"type":"STRING","widget":{"name":"checkpoint_sub_dir"},"link":null},{"localized_name":"force_disable_xformers","name":"force_disable_xformers","shape":7,"type":"BOOLEAN","widget":{"name":"force_disable_xformers"},"link":null}],"outputs":[{"localized_name":"pipe","name":"pipe","type":"DIFFUSERS_PIPE","links":[9]}],"properties":{"Node name for S&R":"[Comfy3D] Load Diffusers Pipeline"},"widgets_values":["TripoSGPipeline","VAST-AI/TripoSG","",false,"",true]},{"id":19,"type":"PreviewImage","pos":[1032.9256591796875,431.04876708984375],"size":[259.2801818847656,478.1733093261719],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":28}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]}],"links":[[1,2,1,1,0,"MASK"],[9,5,0,10,0,"DIFFUSERS_PIPE"],[11,10,0,11,0,"MESH"],[12,11,0,13,0,"MESH"],[13,13,0,12,0,"STRING"],[14,10,0,14,0,"MESH"],[15,14,0,15,0,"STRING"],[17,4,0,17,0,"IMAGE"],[22,2,0,18,0,"IMAGE"],[24,1,0,18,1,"MASK"],[25,18,0,4,0,"IMAGE"],[26,18,1,4,1,"MASK"],[27,4,0,10,1,"IMAGE"],[28,18,0,19,0,"IMAGE"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.8264462809917354,"offset":[41.134269747098244,8.720844783035172]},"frontendVersion":"1.17.11"},"version":0.4}
\ No newline at end of file
diff --git a/_Example_Workflows/_Example_Inputs_Files/bear_back.png b/_Example_Workflows/_Example_Inputs_Files/bear_back.png
new file mode 100644
index 0000000..26685f5
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diff --git a/_Example_Workflows/_Example_Inputs_Files/bear_front.png b/_Example_Workflows/_Example_Inputs_Files/bear_front.png
new file mode 100644
index 0000000..95053ac
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diff --git a/_Example_Workflows/_Example_Inputs_Files/bear_left.png b/_Example_Workflows/_Example_Inputs_Files/bear_left.png
new file mode 100644
index 0000000..c58da0e
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diff --git a/_Example_Workflows/_Example_Inputs_Files/matermulti.png b/_Example_Workflows/_Example_Inputs_Files/matermulti.png
new file mode 100644
index 0000000..cdf5698
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diff --git a/_Example_Workflows/_Example_Inputs_Files/penguin.png b/_Example_Workflows/_Example_Inputs_Files/penguin.png
new file mode 100644
index 0000000..8785186
Binary files /dev/null and b/_Example_Workflows/_Example_Inputs_Files/penguin.png differ
diff --git a/_Pre_Builds/_Build_Scripts/auto_build_all.py b/_Pre_Builds/_Build_Scripts/auto_build_all.py
index f12c923..09e084f 100644
--- a/_Pre_Builds/_Build_Scripts/auto_build_all.py
+++ b/_Pre_Builds/_Build_Scripts/auto_build_all.py
@@ -13,6 +13,7 @@ from build_utils import (
     calculate_runtime,
     git_folder_parallel,
     install_remote_packages,
+    install_platform_packages,
     build_config,
     PYTHON_PATH,
     DEPENDENCIES_FILE_ABS_PATH,
@@ -49,6 +50,7 @@ def setup_build_env():
     subprocess.run([PYTHON_PATH, "-s", "-m", "pip", "install", "-r", BUILD_REQUIREMENTS_FILE_ABS_PATH])
     
     install_remote_packages(build_config.build_base_packages)
+    install_platform_packages()
 
 def clone_or_update_read_dependency(dependency_url, dependency_dir):
     if os.path.exists(dependency_dir):
diff --git a/_Pre_Builds/_Build_Scripts/build_config.yaml b/_Pre_Builds/_Build_Scripts/build_config.yaml
index 47de545..daea55d 100644
--- a/_Pre_Builds/_Build_Scripts/build_config.yaml
+++ b/_Pre_Builds/_Build_Scripts/build_config.yaml
@@ -1,6 +1,6 @@
 repo_id: "MrForExample/Comfy3D_Pre_Builds"
-supported_cuda_versions: ["12.4", "12.1", "11.8"]
-cuda_version: "12.4"
+supported_cuda_versions: ["12.8", "12.6", "12.4", "12.1", "11.8"]
+cuda_version: "12.8"
 
 # Packages inside this file will be pip downloaded before actual build start
 build_requirements: "build_requirements.txt"
@@ -13,19 +13,27 @@ libs_dir_name: "_Libs"
 # Folder used to store built wheels
 wheels_dir_name: "_Build_Wheels"
 
-build_base_packages: ["torch", "torchvision", "xformers", "spconv-${cuda_version}"]
+build_base_packages: ["torch", "torchvision", "xformers"]
+
+# Platform-specific packages
+platform_packages:
+  win:
+    - "triton-windows<3.4"
+  linux:
+    - "triton"
+
 # All packages will be automatically installed at beginning of install.py follwing orders from top to bottom 
 remote_packages:
   torch:
-    version: "2.5.1"
+    version: "2.7.0"
     url: "https://download.pytorch.org/whl/${cuda_version}"
   
   torchvision:
-    version: "0.20.1"
+    version: "0.22.0"
     url: "https://download.pytorch.org/whl/${cuda_version}"
 
   xformers:
-    version: "0.0.28.post3"
+    version: "0.0.30"
     url: "https://download.pytorch.org/whl/${cuda_version}"
 
   # At the time of writing, torch-scatter is not available for PyTorch 2.5.0
diff --git a/_Pre_Builds/_Build_Scripts/build_requirements.txt b/_Pre_Builds/_Build_Scripts/build_requirements.txt
index f58dc0d..6581f0f 100644
--- a/_Pre_Builds/_Build_Scripts/build_requirements.txt
+++ b/_Pre_Builds/_Build_Scripts/build_requirements.txt
@@ -1,6 +1,7 @@
 # build tools
 cmake
 ninja
+pccm
 
 # web tools
 PyGithub
diff --git a/_Pre_Builds/_Build_Scripts/build_utils.py b/_Pre_Builds/_Build_Scripts/build_utils.py
index 21e490c..559cb22 100644
--- a/_Pre_Builds/_Build_Scripts/build_utils.py
+++ b/_Pre_Builds/_Build_Scripts/build_utils.py
@@ -4,6 +4,7 @@ from os.path import dirname
 import platform
 import subprocess
 import time
+import glob
 
 PYTHON_PATH = sys.executable
 
@@ -40,13 +41,22 @@ def get_pytorch_version():
 
 def get_cuda_version():
     # Output: e.g. "cu121" or cu118
-    result = subprocess.run(["nvcc", "--version"], text=True, capture_output=True)
-    if result.returncode == 0:
-        for cuda_version in build_config.supported_cuda_versions:
-            if "cuda_" + cuda_version in result.stdout:
-                return "cu" + cuda_version.replace(".", "")
+    try:
+        result = subprocess.run(["nvcc", "--version"], text=True, capture_output=True)
+        if result.returncode == 0:
+            for cuda_version in build_config.supported_cuda_versions:
+                if "cuda_" + cuda_version in result.stdout:
+                    return "cu" + cuda_version.replace(".", "")
         
-    raise RuntimeError(f"Please install/reinstall CUDA tookit with any of the following supported version: {build_config.supported_cuda_versions}")
+        # If nvcc command succeeded but no supported CUDA version found, use default
+        print(f"Warning: No supported CUDA version detected, using default version: {build_config.cuda_version}")
+        return "cu" + build_config.cuda_version.replace(".", "")
+        
+    except Exception as e:
+        # If nvcc command failed or any other error occurred, CUDA is not installed
+        print("CUDA toolkit not found. Please install CUDA 12.8:")
+        print("https://developer.nvidia.com/cuda-12-8-0-download-archive")
+        sys.exit(1)
 
 OS_TYPE = get_os_type()
 PYTHON_VERSION = get_python_version()
@@ -138,12 +148,76 @@ def git_folder_parallel(repo_id: str, folder: str, recursive: bool=True, root_ou
         print(f"Couldn't download folder {folder} from repo {repo_id}", e)
         return False
     
+def is_package_installed(package_name, required_version=None):
+    """Check if a package is installed with the required version"""
+    try:
+        import importlib.metadata
+        installed_version = importlib.metadata.version(package_name)
+        
+        if required_version is None:
+            return True
+        
+        # Handle versions with suffixes like "2.7.1+cu128"
+        installed_base_version = installed_version.split('+')[0]
+        required_base_version = required_version.split('+')[0]
+        
+        # For PyTorch-related packages, we're more flexible with patch versions
+        if package_name in ['torch', 'torchvision', 'xformers']:
+            # Parse version components
+            try:
+                installed_parts = [int(x) for x in installed_base_version.split('.')]
+                required_parts = [int(x) for x in required_base_version.split('.')]
+                print(f"[DEBUG] {package_name}: version parts - installed={installed_parts}, required={required_parts}")
+                
+                # Pad with zeros if needed (e.g., "2.7" vs "2.7.0")
+                max_len = max(len(installed_parts), len(required_parts))
+                installed_parts.extend([0] * (max_len - len(installed_parts)))
+                required_parts.extend([0] * (max_len - len(required_parts)))
+                print(f"[DEBUG] {package_name}: padded parts - installed={installed_parts}, required={required_parts}")
+                
+                # Check major.minor compatibility, allow newer patch versions
+                if len(installed_parts) >= 2 and len(required_parts) >= 2:
+                    # Major and minor versions must match
+                    major_minor_match = (installed_parts[0] == required_parts[0] and 
+                                       installed_parts[1] == required_parts[1])
+                    
+                    if major_minor_match:
+                        # Patch version can be equal or higher
+                        if len(installed_parts) >= 3 and len(required_parts) >= 3:
+                            patch_ok = installed_parts[2] >= required_parts[2]
+                            return patch_ok
+                        return True
+                return False
+            except ValueError as e:
+                # If version parsing fails, fall back to exact match
+                exact_match = installed_base_version == required_base_version
+                return exact_match
+        else:
+            # For other packages, require exact version match
+            exact_match = installed_base_version == required_base_version
+            return exact_match
+            
+    except (importlib.metadata.PackageNotFoundError, Exception) as e:
+        return False
+
 def install_remote_packages(package_names):
     for package_name in package_names:
+        original_package_name = package_name
+        required_version = None
+        
         if package_name in build_config.remote_packages:
             package_attr = build_config.remote_packages[package_name]
             if hasattr(package_attr, "version"):
-                package_name += f"=={package_attr.version}"
+                required_version = package_attr.version
+                package_name += f"=={required_version}"
+            
+            # Check if package is already installed with the correct version
+            if is_package_installed(original_package_name, required_version):
+                print(f"Package {original_package_name} (version {required_version or 'any'}) is already installed, skipping...")
+                continue
+            
+            print(f"Installing {original_package_name} version {required_version or 'latest'}...")
+            
             if hasattr(package_attr, "url"):
                 url_option = package_attr.url_option if hasattr(package_attr, "url_option") else "--index-url"
                 
@@ -152,5 +226,34 @@ def install_remote_packages(package_names):
                     package_name, url_option, package_attr.url
                 ])
                 continue
+        else:
+            # Check if package is already installed
+            if is_package_installed(original_package_name):
+                print(f"Package {original_package_name} is already installed, skipping...")
+                continue
+            
+            print(f"Installing {original_package_name}...")
 
-        subprocess.run([PYTHON_PATH, "-s", "-m", "pip", "install", package_name])
\ No newline at end of file
+        subprocess.run([PYTHON_PATH, "-s", "-m", "pip", "install", package_name])
+
+def install_platform_packages():
+    if hasattr(build_config, 'platform_packages') and OS_TYPE in build_config.platform_packages:
+        packages = build_config.platform_packages[OS_TYPE]
+        for package in packages:
+            # Extract package name (without version constraints)
+            package_name = package.split('==')[0].split('>=')[0].split('<=')[0].split('<')[0].split('>')[0]
+            
+            if is_package_installed(package_name):
+                print(f"Platform package {package_name} is already installed, skipping...")
+                continue
+            
+            print(f"Installing platform package {package}...")
+            subprocess.run([PYTHON_PATH, "-s", "-m", "pip", "install", package])
+
+def wheels_dir_exists_and_not_empty(builds_dir):
+    if not os.path.exists(builds_dir):
+        return False
+    
+    # Check if directory has any .whl files
+    wheel_files = glob.glob(os.path.join(builds_dir, "**/*.whl"), recursive=True)
+    return len(wheel_files) > 0
\ No newline at end of file
diff --git a/_Pre_Builds/_Build_Scripts/dependencies.txt b/_Pre_Builds/_Build_Scripts/dependencies.txt
index a7c08db..7244097 100644
--- a/_Pre_Builds/_Build_Scripts/dependencies.txt
+++ b/_Pre_Builds/_Build_Scripts/dependencies.txt
@@ -1,3 +1,4 @@
+https://github.com/traveller59/spconv.git
 https://github.com/ashawkey/diff-gaussian-rasterization.git
 https://github.com/NVlabs/nvdiffrast.git
 https://github.com/ashawkey/kiuikit.git
diff --git a/__init__.py b/__init__.py
index 4040c2c..cc8c101 100644
--- a/__init__.py
+++ b/__init__.py
@@ -4,7 +4,8 @@ import folder_paths as comfy_paths
 from pyhocon import ConfigFactory
 import logging
 
-ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack")
+# ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack")
+ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
 MODULE_PATH = os.path.join(ROOT_PATH, "Gen_3D_Modules")
 MV_ALGO_PATH = os.path.join(ROOT_PATH, "MVs_Algorithms")
 
diff --git a/install.py b/install.py
index 0fb4124..e2c0c40 100644
--- a/install.py
+++ b/install.py
@@ -20,6 +20,8 @@ try:
         get_platform_config_name,
         git_folder_parallel,
         install_remote_packages,
+        install_platform_packages,
+        wheels_dir_exists_and_not_empty,
         build_config,
         PYTHON_PATH,
         WHEELS_ROOT_ABS_PATH,
@@ -27,6 +29,12 @@ try:
     )
     from shared_utils.log_utils import cstr
     
+    # Ensure PyGithub is installed for downloading wheels
+    try:
+        import github
+    except ImportError:
+        subprocess.run([sys.executable, "-m", "pip", "install", "PyGithub"])
+    
     def try_auto_build_all(builds_dir):
         cstr(f"Try building all required packages...").msg.print()
         result = subprocess.run(
@@ -48,6 +56,7 @@ try:
     
     # Install packages that needs specify remote url
     install_remote_packages(build_config.build_base_packages)
+    install_platform_packages()
     
     # Get the target remote pre-built wheels directory name and path
     platform_config_name = get_platform_config_name()
@@ -56,8 +65,12 @@ try:
     builds_dir = os.path.join(WHEELS_ROOT_ABS_PATH, platform_config_name)
     
     build_succeed = False
+    # Check if wheels already exist locally
+    if wheels_dir_exists_and_not_empty(builds_dir):
+        cstr(f"Found existing wheels in {builds_dir}").msg.print()
+        build_succeed = True
     # Download pre-build wheels if exist
-    if git_folder_parallel(build_config.repo_id, remote_builds_dir_name, recursive=True, root_outdir=builds_dir):
+    elif git_folder_parallel(build_config.repo_id, remote_builds_dir_name, recursive=True, root_outdir=builds_dir):
         build_succeed = True
     # Build the wheels if couldn't find pre-build wheels
     elif try_auto_build_all(builds_dir):
diff --git a/mesh_processer/mesh_utils.py b/mesh_processer/mesh_utils.py
index a70b61b..c3799df 100644
--- a/mesh_processer/mesh_utils.py
+++ b/mesh_processer/mesh_utils.py
@@ -242,6 +242,8 @@ def decimate_mesh(
         m = pml.Mesh(verts, faces)
         ms = pml.MeshSet()
         ms.add_mesh(m, "mesh")  # will copy!
+        
+        ms.meshing_merge_close_vertices()
 
         # filters
         # ms.meshing_decimation_clustering(threshold=pml.PercentageValue(1))
diff --git a/nodes.py b/nodes.py
index 803ee8e..faac023 100644
--- a/nodes.py
+++ b/nodes.py
@@ -30,6 +30,7 @@ from diffusers import (
     KDPM2AncestralDiscreteScheduler,
     KDPM2DiscreteScheduler,
 )
+
 from huggingface_hub import snapshot_download
 
 from plyfile import PlyData
@@ -97,8 +98,21 @@ from Hunyuan3D_V1.mvd.hunyuan3d_mvd_lite_pipeline import Hunyuan3D_MVD_Lite_Pipe
 from Hunyuan3D_V1.infer import Views2Mesh
 from Hunyuan3D_V2.hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, Hunyuan3DDiTFlowMatchingPipeline
 from Hunyuan3D_V2.hy3dgen.texgen import Hunyuan3DPaintPipeline
+from Hunyuan3D_V2.hy3dgen.rembg import BackgroundRemover
 from TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline
 from TRELLIS.trellis.utils import postprocessing_utils
+from TripoSG.pipelines.pipeline_triposg import TripoSGPipeline
+from TripoSG.pipelines.pipeline_triposg_scribble import TripoSGScribblePipeline
+from Stable3DGen.pipeline_builders import StableGenPipelineBuilder
+from MV_Adapter.mvadapter_node_utils import (
+        prepare_pipeline as mvadapter_prepare_pipeline,
+        run_pipeline as mvadapter_run_pipeline, 
+        prepare_tg2mv_pipeline as mvadapter_prepare_tg2mv_pipeline,
+        run_tg2mv_pipeline as mvadapter_run_tg2mv_pipeline,
+        prepare_texture_pipeline as mvadapter_prepare_texture_pipeline,
+        download_texture_checkpoints,
+    )
+from MV_Adapter.mvadapter.utils import make_image_grid
 
 os.environ['SPCONV_ALGO'] = 'native'
 
@@ -126,6 +140,8 @@ DIFFUSERS_PIPE_DICT = OrderedDict([
     ("Hunyuan3DMVDLitePipeline", Hunyuan3D_MVD_Lite_Pipeline),
     ("Hunyuan3DDiTFlowMatchingPipeline", Hunyuan3DDiTFlowMatchingPipeline),
     ("Hunyuan3DPaintPipeline", Hunyuan3DPaintPipeline),
+    ("TripoSGPipeline", TripoSGPipeline),
+    ("TripoSGScribblePipeline", TripoSGScribblePipeline),
 ])
 
 DIFFUSERS_SCHEDULER_DICT = OrderedDict([
@@ -139,7 +155,7 @@ DIFFUSERS_SCHEDULER_DICT = OrderedDict([
     ("KDPM2DiscreteScheduler,", KDPM2DiscreteScheduler),
 ])
 
-ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack")
+ROOT_PATH = os.path.dirname(os.path.realpath(__file__))
 CKPT_ROOT_PATH = os.path.join(ROOT_PATH, "Checkpoints")
 CKPT_DIFFUSERS_PATH = os.path.join(CKPT_ROOT_PATH, "Diffusers")
 CONFIG_ROOT_PATH = os.path.join(ROOT_PATH, "Configs")
@@ -176,6 +192,7 @@ DEVICE = torch.device(DEVICE_STR)
 
 HF_DOWNLOAD_IGNORE = ["*.yaml", "*.json", "*.py", ".png", ".jpg", ".gif"]
 
+
 class Preview_3DGS:
 
     @classmethod
@@ -632,11 +649,9 @@ class Decimate_Mesh:
     CATEGORY = "Comfy3D/Preprocessor"
 
     def process_mesh(self, mesh, target, remesh, optimalplacement):
-        
-        vertices, faces = decimate_mesh(mesh.v, mesh.f, target, remesh, optimalplacement)
-        mesh.v = vertices
-        mesh.f = faces
-
+        vertices, faces = decimate_mesh(mesh.v.detach().cpu().numpy(), mesh.f.detach().cpu().numpy(), target, remesh, optimalplacement)
+        mesh.v, mesh.f = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE)
+        mesh.auto_normal()
         return (mesh,)
 
 class Switch_3DGS_Axis:
@@ -1486,6 +1501,7 @@ class Load_Diffusers_Pipeline:
             },
             "optional": {
                 "checkpoint_sub_dir": ("STRING", {"default": "", "multiline": False}),
+                "force_disable_xformers": ("BOOLEAN", {"default": False}),
             }
         }
     
@@ -1498,7 +1514,7 @@ class Load_Diffusers_Pipeline:
     FUNCTION = "load_diffusers_pipe"
     CATEGORY = "Comfy3D/Import|Export"
     
-    def load_diffusers_pipe(self, diffusers_pipeline_name, repo_id, custom_pipeline, force_download, checkpoint_sub_dir=""):
+    def load_diffusers_pipe(self, diffusers_pipeline_name, repo_id, custom_pipeline, force_download, checkpoint_sub_dir="", force_disable_xformers=False):
         
         # resume download pretrained checkpoint
         ckpt_download_dir = os.path.join(CKPT_DIFFUSERS_PATH, repo_id)
@@ -1515,9 +1531,9 @@ class Load_Diffusers_Pipeline:
             ckpt_path,
             torch_dtype=WEIGHT_DTYPE,
             custom_pipeline=custom_pipeline,
-        ).to(DEVICE)
+        ).to(DEVICE, WEIGHT_DTYPE)
         
-        if hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
+        if hasattr(pipe, 'enable_xformers_memory_efficient_attention') and not force_disable_xformers:
             pipe.enable_xformers_memory_efficient_attention()
         
         return (pipe, )
@@ -3920,7 +3936,8 @@ class Hunyuan3D_V1_Reconstruction_Model:
         mesh.auto_normal()
         
         return (mesh,)
-
+    
+# deprecated
 class Hunyuan3D_V2_DiT_Flow_Matching_Model:
     
     @classmethod
@@ -3976,6 +3993,7 @@ class Hunyuan3D_V2_DiT_Flow_Matching_Model:
 
         return (mesh,)
 
+# deprecated
 class Hunyuan3D_V2_Paint_Model:
     
     @classmethod
@@ -4087,36 +4105,1077 @@ class Trellis_Structured_3D_Latents_Models:
     ):
         single_image = torch_imgs_to_pils(reference_image, reference_mask)[0]
 
+        outputs = trellis_pipe.run(
+            single_image,
+            # Optional parameters
+            seed=seed,
+            formats=["gaussian", "mesh"],
+            sparse_structure_sampler_params={
+                "cfg_strength": sparse_structure_guidance_scale,
+                "steps": sparse_structure_sample_steps,
+            },
+            slat_sampler_params={
+                "cfg_strength": structured_latent_guidance_scale,
+                "steps": structured_latent_sample_steps,
+            },
+        )
+
+        # GLB files can be extracted from the outputs
+        vertices, faces, uvs, texture = postprocessing_utils.finalize_mesh(
+            outputs['gaussian'][0],
+            outputs['mesh'][0],
+            # Optional parameters
+            simplify=0.95,          # Ratio of triangles to remove in the simplification process
+            texture_size=1024,      # Size of the texture used for the GLB
+        )
+
+        vertices, faces, uvs, texture = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE), torch.from_numpy(uvs).to(DEVICE), torch.from_numpy(texture).to(DEVICE)
+        mesh = Mesh(v=vertices, f=faces, vt=uvs, ft=faces, albedo=texture, device=DEVICE)
+        mesh.auto_normal()
+
+        return (mesh,)
+
+class TripoSG_I23D_Model:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "tsg_pipe": ("DIFFUSERS_PIPE",),
+                "reference_image": ("IMAGE",),
+                "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}),
+                "guidance_scale": ("FLOAT", {"default": 7.0, "min": 0.0, "step": 0.01}),
+                "num_inference_steps": ("INT", {"default": 50, "min": 1}),
+                "use_flash_decoder": ("BOOLEAN", {"default": True}),
+                "flash_octree_depth": ("INT", {"default": 9, "min": 1}),
+                "hierarchical_octree_depth": ("INT", {"default": 9, "min": 1}),
+                "dense_octree_depth": ("INT", {"default": 8, "min": 1}),
+            }
+        }
+    
+    RETURN_TYPES = (
+        "MESH",
+    )
+    RETURN_NAMES = (
+        "mesh",
+    )
+    FUNCTION = "run_model"
+    CATEGORY = "Comfy3D/Algorithm"
+    
+    @torch.no_grad()
+    def run_model(
+        self, 
+        tsg_pipe, 
+        reference_image, # [1, H, W, 3]
+        seed,
+        guidance_scale,
+        num_inference_steps,
+        use_flash_decoder,
+        flash_octree_depth,
+        hierarchical_octree_depth,
+        dense_octree_depth,
+    ):
+        
+        single_image = torch_imgs_to_pils(reference_image)[0]
+        
         with torch.inference_mode(False):
-            outputs = trellis_pipe.run(
-                single_image,
-                # Optional parameters
-                seed=seed,
-                formats=["gaussian", "mesh"],
-                sparse_structure_sampler_params={
-                    "cfg_strength": sparse_structure_guidance_scale,
-                    "steps": sparse_structure_sample_steps,
-                },
-                slat_sampler_params={
-                    "cfg_strength": structured_latent_guidance_scale,
-                    "steps": structured_latent_sample_steps,
-                },
-            )
+            outputs = tsg_pipe(
+                image=single_image,
+                generator=torch.Generator(device=DEVICE).manual_seed(seed),
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                use_flash_decoder=use_flash_decoder,
+                flash_octree_depth=flash_octree_depth,
+                hierarchical_octree_depth=hierarchical_octree_depth,
+                dense_octree_depth=dense_octree_depth,
+            ).samples[0]
 
-            # GLB files can be extracted from the outputs
-            vertices, faces, uvs, texture = postprocessing_utils.finalize_mesh(
-                outputs['gaussian'][0],
-                outputs['mesh'][0],
-                # Optional parameters
-                simplify=0.95,          # Ratio of triangles to remove in the simplification process
-                texture_size=1024,      # Size of the texture used for the GLB
-            )
-
-            vertices, faces, uvs, texture = torch.from_numpy(vertices).to(DEVICE), torch.from_numpy(faces).to(torch.int64).to(DEVICE), torch.from_numpy(uvs).to(DEVICE), torch.from_numpy(texture).to(DEVICE)
-            mesh = Mesh(v=vertices, f=faces, vt=uvs, ft=faces, albedo=texture, device=DEVICE)
+            vertices, faces = torch.from_numpy(outputs[0].astype(np.float32)).to(DEVICE), torch.from_numpy(np.ascontiguousarray(outputs[1])).to(torch.int64).to(DEVICE) 
+            mesh = Mesh(v=vertices, f=faces, device=DEVICE)
             mesh.auto_normal()
 
         return (mesh,)
     
-
+class TripoSG_Scribble_Model:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "tsg_scribble_pipe": ("DIFFUSERS_PIPE",),
+                "scribble_image": ("IMAGE",),
+                "prompt": ("STRING", {
+                    "default": "3D assets",
+                    "multiline": True
+                }),
+                "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}),
+                "num_inference_steps": ("INT", {"default": 16, "min": 1}),
+                "scribble_confidence": ("FLOAT", {"default": 0.4, "min": 0.0, "step": 0.01}),
+                "prompt_confidence": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.01}),
+                "use_flash_decoder": ("BOOLEAN", {"default": False}),
+                "flash_octree_depth": ("INT", {"default": 8, "min": 1}),
+                "hierarchical_octree_depth": ("INT", {"default": 8, "min": 1}),
+                "dense_octree_depth": ("INT", {"default": 8, "min": 1}),
+            }
+        }
     
+    RETURN_TYPES = (
+        "MESH",
+    )
+    RETURN_NAMES = (
+        "mesh",
+    )
+    FUNCTION = "run_model"
+    CATEGORY = "Comfy3D/Algorithm"
+    
+    @torch.no_grad()
+    def run_model(
+        self, 
+        tsg_scribble_pipe, 
+        scribble_image, # [1, H, W, 3]
+        prompt,
+        seed,
+        num_inference_steps,
+        scribble_confidence,
+        prompt_confidence,
+        use_flash_decoder,
+        flash_octree_depth,
+        hierarchical_octree_depth,
+        dense_octree_depth,
+    ):
+        
+        single_image = torch_imgs_to_pils(scribble_image)[0]
+        
+        outputs = tsg_scribble_pipe(
+            image=single_image,
+            prompt=prompt,
+            generator=torch.Generator(device=DEVICE).manual_seed(seed),
+            num_inference_steps=num_inference_steps,
+            guidance_scale=0, # this is a CFG-distilled model
+            attention_kwargs={"cross_attention_scale": prompt_confidence, "cross_attention_2_scale": scribble_confidence},
+            use_flash_decoder=use_flash_decoder,
+            flash_octree_depth=flash_octree_depth, # there're some boundary problems when using flash decoder with this model
+            hierarchical_octree_depth=hierarchical_octree_depth,
+            dense_octree_depth=dense_octree_depth,
+        ).samples[0]
+
+        vertices, faces = torch.from_numpy(outputs[0].astype(np.float32)).to(DEVICE), torch.from_numpy(np.ascontiguousarray(outputs[1])).to(torch.int64).to(DEVICE)
+        mesh = Mesh(v=vertices, f=faces.to(torch.int64), device=DEVICE)
+        mesh.auto_normal()
+
+        return (mesh,)
+
+class Load_Hunyuan3D_V2_ShapeGen_Pipeline:
+    CATEGORY      = "Comfy3D/Algorithm"
+    RETURN_TYPES  = ("DIFFUSERS_PIPE",)
+    RETURN_NAMES  = ("shapegen_pipe",)
+    FUNCTION      = "load"
+
+    _REPO_ID_BASE = "tencent"
+
+    _MODES = {
+        "Hunyuan3D-2":             ("Hunyuan3D-2",     "hunyuan3d-dit-v2-0",         30),
+        "Hunyuan3D-2-Fast":        ("Hunyuan3D-2",     "hunyuan3d-dit-v2-0-fast",    20),
+        "Hunyuan3D-2-Turbo":       ("Hunyuan3D-2",     "hunyuan3d-dit-v2-0-turbo",    5),
+        "Hunyuan3D-2mini":         ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini",       30),
+        "Hunyuan3D-2mini-Fast":    ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-fast",   20),
+        "Hunyuan3D-2mini-Turbo":   ("Hunyuan3D-2mini", "hunyuan3d-dit-v2-mini-turbo",  5),
+        "Hunyuan3D-2mv":           ("Hunyuan3D-2mv",   "hunyuan3d-dit-v2-mv",   30),
+        "Hunyuan3D-2mv-Fast":      ("Hunyuan3D-2mv",   "hunyuan3d-dit-v2-mv-fast",    20),
+        "Hunyuan3D-2mv-Turbo":     ("Hunyuan3D-2mv",   "hunyuan3d-dit-v2-mv-turbo",   5),
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "generation_mode": (list(cls._MODES.keys()),),
+                "weights_format" : (["safetensors", "ckpt"],),
+                "flash_vdm"      : ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    @staticmethod
+    def _ensure_weights(repo: str, subfolder: str, use_safetensors: bool):
+        base_dir = os.path.join(CKPT_DIFFUSERS_PATH, f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}")
+        ckpt_file = "model.fp16.safetensors" if use_safetensors else "model.fp16.ckpt"
+        ckpt_path = os.path.join(base_dir, subfolder, ckpt_file)
+
+        if not os.path.exists(ckpt_path):
+            snapshot_download(
+                repo_id=f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}",
+                repo_type="model",
+                local_dir=base_dir,
+                resume_download=True,
+                ignore_patterns = HF_DOWNLOAD_IGNORE
+            )
+
+    @staticmethod
+    def _build_pipe(repo: str, subfolder: str, use_safetensors: bool, flash_vdm: bool):
+        Load_Hunyuan3D_V2_ShapeGen_Pipeline._ensure_weights(repo, subfolder, use_safetensors)
+
+        model_dir = os.path.join(CKPT_DIFFUSERS_PATH,
+                                 f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}",
+                                 subfolder)
+        ckpt = os.path.join(model_dir, "model.fp16.safetensors" if use_safetensors else "model.fp16.ckpt")
+        cfg  = os.path.join(model_dir, "config.yaml")
+
+        pipe = Hunyuan3DDiTFlowMatchingPipeline.from_single_file(
+            ckpt_path=ckpt,
+            config_path=cfg,
+            device="cuda",
+            dtype=torch.float16,
+            use_safetensors=use_safetensors,
+            from_pretrained_kwargs={
+                "model_path": f"{Load_Hunyuan3D_V2_ShapeGen_Pipeline._REPO_ID_BASE}/{repo}",
+                "subfolder": subfolder,
+                "use_safetensors": use_safetensors,
+            },
+        )
+
+        if flash_vdm and any(tag in subfolder for tag in ("turbo", "fast")):
+            pipe.enable_flashvdm(replace_vae=False)
+
+        return pipe.to("cuda", torch.float16)
+
+    def load(self, generation_mode, weights_format, flash_vdm):
+        repo, subfolder, def_steps = self._MODES[generation_mode]
+        use_safe = (weights_format == "safetensors")
+        pipe = self._build_pipe(repo, subfolder, use_safe, flash_vdm)
+        pipe.num_inference_steps = def_steps
+        return (pipe,)
+    
+class Load_Hunyuan3D_V2_TexGen_Pipeline: 
+    CATEGORY     = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("DIFFUSERS_PIPE",)
+    RETURN_NAMES = ("texgen_pipe",)
+    FUNCTION     = "load"
+
+    MODEL2REPO = {
+        "Standard": ("tencent/Hunyuan3D-2", "hunyuan3d-paint-v2-0"),
+        "Turbo":    ("tencent/Hunyuan3D-2", "hunyuan3d-paint-v2-0-turbo"),
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "generation_mode": (list(cls.MODEL2REPO.keys()),),
+        }}
+
+    def _download_required_weights(self, repo_id, subfolder):
+        ckpt_download_dir = os.path.join(CKPT_DIFFUSERS_PATH, repo_id)
+        os.makedirs(ckpt_download_dir, exist_ok=True)
+
+        # Only download the "delight" and target submodel directories
+        for folder in ["hunyuan3d-delight-v2-0", subfolder]:
+            snapshot_download(
+                repo_id=repo_id,
+                local_dir=ckpt_download_dir,
+                repo_type="model",
+                force_download=False,
+                ignore_patterns=HF_DOWNLOAD_IGNORE
+            )
+
+    def load(self, generation_mode):
+        repo_id, subfolder = self.MODEL2REPO[generation_mode]
+
+        self._download_required_weights(repo_id, subfolder)
+
+        local_repo_dir = os.path.join(CKPT_DIFFUSERS_PATH, repo_id)
+
+        pipe = Hunyuan3DPaintPipeline.from_pretrained(
+            model_path=local_repo_dir,
+            subfolder=subfolder
+        )
+
+        return (pipe.to("cuda", torch.float16),)
+
+class Hunyuan3D_V2_Paint_Model_Turbo_MV:
+    """
+    Texture-painting pipeline using a list of PIL images.
+    If list contains 1 image → single-view; if >1 → multi-view mode.
+    """
+
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("MESH",)
+    RETURN_NAMES = ("mesh",)
+    FUNCTION = "run"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "hunyuan3d_v2_texgen_pipe": ("DIFFUSERS_PIPE",),
+                "mesh": ("MESH",),
+                "images": ("LIST",),
+            }
+        }
+
+    @torch.no_grad()
+    def run(self, hunyuan3d_v2_texgen_pipe, mesh, images):
+        if not isinstance(images, list) or len(images) == 0:
+            raise Exception("[Hunyuan3D_V2_Paint_Model_Turbo_MV] 'images' must be a non-empty list of PIL images")
+
+        v_np = mesh.v.detach().cpu().numpy()
+        f_np = mesh.f.detach().cpu().numpy()
+        tri = trimesh.Trimesh(vertices=v_np, faces=f_np)
+
+        try:
+            textured = hunyuan3d_v2_texgen_pipe(tri, images)
+        except Exception as e:
+            raise Exception(f"[Hunyuan3D_V2_Paint_Model_Turbo_MV] Texture generation failed: {str(e)}")
+
+        m_out = Mesh.load_trimesh(given_mesh=textured)
+        m_out.auto_normal()
+        return (m_out,)
+
+class Multi_Background_Remover:
+    """
+    Converts 1 to 4 image inputs (front/back/left/right) to a list of processed PIL images.
+    Applies RGBA conversion and background removal.
+    Suitable for feeding directly into ShapeGen or Paint models.
+    """
+
+    CATEGORY = "Comfy3D/Preprocessors"
+    RETURN_TYPES = ("LIST",)  # List of PIL images
+    RETURN_NAMES = ("images",)
+    FUNCTION = "run"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image_front": ("IMAGE",),
+            },
+            "optional": {
+                "image_back": ("IMAGE",),
+                "image_left": ("IMAGE",),
+            }
+        }
+
+    @torch.no_grad()
+    def run(
+        self,
+        image_front,
+        image_back=None,
+        image_left=None,
+        image_right=None
+    ):
+        rmbg = BackgroundRemover()
+
+        mv_inputs = {
+            k: v for k, v in {
+                "front": image_front,
+                "back": image_back,
+                "left": image_left,
+                "right": image_right
+            }.items() if v is not None
+        }
+
+        images = []
+        for key, tensor_img in mv_inputs.items():
+            pil_img = torch_imgs_to_pils(tensor_img)[0].convert("RGBA")
+            if pil_img.mode == "RGB":
+                pil_img = rmbg(pil_img.convert("RGB"))
+            images.append(pil_img)
+
+        return (images,)        
+
+class Hunyuan3D_V2_ShapeGen_MV:
+    """
+    Shape generation pipeline using a list of processed PIL images.
+    If len(images) == 1 → single-view; if >1 → multi-view dict.
+    """
+
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("MESH",)
+    RETURN_NAMES = ("mesh",)
+    FUNCTION = "run"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "shapegen_pipe": ("DIFFUSERS_PIPE",),
+                "images": ("LIST",),
+                "seed": ("INT", {"default": 1234, "min": 0, "max": 0xffffffffffffffff}),
+                "guidance_scale": ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}),
+                "num_inference_steps": ("INT", {"default": 5, "min": 0, "tooltip": "Turbo: 5; Fast/Standard: 30–40"}),
+                "octree_resolution": ("INT", {"default": 256, "min": 64}),
+            }
+        }
+
+    @torch.no_grad()
+    def run(
+        self,
+        shapegen_pipe,
+        images,
+        seed=1234,
+        guidance_scale=5.0,
+        num_inference_steps=5,
+        octree_resolution=256
+    ):
+        if not isinstance(images, list) or len(images) == 0:
+            raise Exception("[Hunyuan3D_V2_ShapeGen_MV] 'images' must be a non-empty list of PIL images")
+
+        if len(images) == 1:
+            image = images[0]
+        else:
+            directions = ["front", "back", "left", "right"]
+            image = {k: v for k, v in zip(directions, images)}
+
+        steps = shapegen_pipe.default_steps if num_inference_steps == 0 else num_inference_steps
+        gen = torch.Generator(device=shapegen_pipe.device).manual_seed(seed)
+
+        try:
+            mesh = shapegen_pipe(
+                image=image,
+                num_inference_steps=steps,
+                guidance_scale=guidance_scale,
+                generator=gen,
+                octree_resolution=octree_resolution,
+                output_type="trimesh",
+            )[0]
+        except Exception as e:
+            raise Exception(f"[Hunyuan3D_V2_ShapeGen_MV] Shape generation failed: {str(e)}")
+
+        for fn in (FloaterRemover(), DegenerateFaceRemover(), FaceReducer()):
+            mesh = fn(mesh)
+
+        return (Mesh.load_trimesh(given_mesh=mesh),)
+
+#--------------------------------
+class Load_StableGen_Trellis_Pipeline:
+    CATEGORY      = "Comfy3D/Algorithm"
+    RETURN_TYPES  = ("DIFFUSERS_PIPE",)
+    RETURN_NAMES  = ("trellis_pipe",)
+    FUNCTION      = "load"
+
+    _REPO_ID_BASE = "Stable-X"
+    CKPT_STABLEGEN_PATH = os.path.join(CKPT_DIFFUSERS_PATH, "Stable3DGen")
+
+    _MODES = {
+        "trellis-normal-v0-1": ("trellis-normal-v0-1", 12, 12),  # (repo, ss_steps, slat_steps)
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        available_attn, available_sparse = StableGenPipelineBuilder.get_available_backends()
+
+        return {
+            "required": {
+                "model_name": (list(cls._MODES.keys()),),
+                "dinov2_model": (["dinov2_vitl14_reg"],),
+                "use_fp16": ("BOOLEAN", {"default": True}),
+                "attn_backend": (available_attn,),
+                "sparse_backend": (available_sparse,),
+                "spconv_algo": (["implicit_gemm", "native", "auto"],),
+                "smooth_k": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    @classmethod
+    def _build_pipe(cls, repo: str, dinov2_model: str, use_fp16: bool, attn_backend: str, 
+                   sparse_backend: str, spconv_algo: str, smooth_k: bool):
+        
+        return StableGenPipelineBuilder.build_trellis_pipeline(
+            repo=repo,
+            dinov2_model=dinov2_model,
+            use_fp16=use_fp16,
+            attn_backend=attn_backend,
+            sparse_backend=sparse_backend,
+            spconv_algo=spconv_algo,
+            smooth_k=smooth_k,
+            ckpt_path=cls.CKPT_STABLEGEN_PATH
+        )
+
+    def load(self, model_name, dinov2_model, use_fp16, attn_backend, sparse_backend, spconv_algo, smooth_k):
+        repo, ss_steps, slat_steps = self._MODES[model_name]
+        
+        pipe = self.__class__._build_pipe(repo, dinov2_model, use_fp16, attn_backend, sparse_backend, spconv_algo, smooth_k)
+        # Store default steps
+        pipe.default_ss_steps = ss_steps
+        pipe.default_slat_steps = slat_steps
+        
+        return (pipe,)
+
+
+class Load_StableGen_StableX_Pipeline:
+    CATEGORY     = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("DIFFUSERS_PIPE",)
+    RETURN_NAMES = ("stablex_pipe",)
+    FUNCTION     = "load"
+
+    _REPO_ID_BASE = "Stable-X"
+    CKPT_STABLEGEN_PATH = os.path.join(CKPT_DIFFUSERS_PATH, "Stable3DGen")
+
+    _MODES = {
+        "yoso-normal-v1-8-1": "yoso-normal-v1-8-1",
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model_name": (list(cls._MODES.keys()),),
+                "use_fp16": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    @classmethod
+    def _build_pipe(cls, repo: str, use_fp16: bool):
+        return StableGenPipelineBuilder.build_stablex_pipeline(
+            repo=repo,
+            use_fp16=use_fp16,
+            ckpt_path=cls.CKPT_STABLEGEN_PATH
+        )
+
+    def load(self, model_name, use_fp16):
+        repo = self._MODES[model_name]
+        pipe = self.__class__._build_pipe(repo, use_fp16)
+        return (pipe,)
+
+
+class StableGen_Trellis_Image_To_3D:
+    """
+    3D generation pipeline using Trellis model.
+    """
+
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("MESH",)
+    RETURN_NAMES = ("mesh",)
+    FUNCTION = "run"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "trellis_pipe": ("DIFFUSERS_PIPE",),
+                "images": ("IMAGE", {"list": True}),
+                "mode": (["single", "multi"], {"default": "single"}),
+                "seed": ("INT", {"default": 1234, "min": 0, "max": 0xffffffffffffffff}),
+                "ss_guidance_strength": ("FLOAT", {"default": 7.5, "min": 0.0, "step": 0.1}),
+                "ss_sampling_steps": ("INT", {"default": 12, "min": 1}),
+                "slat_guidance_strength": ("FLOAT", {"default": 3.0, "min": 0.0, "step": 0.1}),
+                "slat_sampling_steps": ("INT", {"default": 12, "min": 1}),
+                "mesh_simplify": ("FLOAT", {"default": 0.95, "min": 0.9, "max": 1.0, "step": 0.01}),
+            }
+        }
+
+    @torch.no_grad()
+    def run(
+        self,
+        trellis_pipe,
+        images,
+        mode="single",
+        seed=1234,
+        ss_guidance_strength=7.5,
+        ss_sampling_steps=12,
+        slat_guidance_strength=3.0,
+        slat_sampling_steps=12,
+        mesh_simplify=0.95
+    ):
+        if isinstance(images, torch.Tensor):
+            images = torch_imgs_to_pils(images)
+        
+        if not isinstance(images, list) or len(images) == 0:
+            raise Exception(f"[StableGen_Trellis_Image_To_3D] 'images' must be a non-empty list of PIL images. Got type: {type(images)}, len: {len(images) if hasattr(images, '__len__') else 'no len'}")
+
+        with trellis_pipe.inference_context():
+            if mode == "single":
+                if len(images) > 1:
+                    print(f"Warning: Single mode selected but {len(images)} images provided. Using first image.")
+                image_input = images[0]
+                
+                pipeline_params = StableGenPipelineBuilder.create_trellis_pipeline_params(
+                    seed=seed,
+                    ss_sampling_steps=ss_sampling_steps,
+                    ss_guidance_strength=ss_guidance_strength,
+                    slat_sampling_steps=slat_sampling_steps,
+                    slat_guidance_strength=slat_guidance_strength,
+                    formats=["mesh"]
+                )
+                
+                outputs = trellis_pipe.run(
+                    image_input,
+                    **pipeline_params
+                )
+            else:  
+                pipeline_params = StableGenPipelineBuilder.create_trellis_pipeline_params(
+                    seed=seed,
+                    ss_sampling_steps=ss_sampling_steps,
+                    ss_guidance_strength=ss_guidance_strength,
+                    slat_sampling_steps=slat_sampling_steps,
+                    slat_guidance_strength=slat_guidance_strength,
+                    formats=["mesh"]
+                )
+                
+                outputs = trellis_pipe.run_multi_image(
+                    images,
+                    **pipeline_params
+                )
+            
+        try:
+            mesh_output = outputs['mesh'][0]
+            
+            vertices = mesh_output.vertices.cpu().numpy()
+            faces = mesh_output.faces.cpu().numpy()
+            
+            transformation_matrix = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
+            vertices = vertices @ transformation_matrix
+            
+            tri_mesh = trimesh.Trimesh(vertices, faces)
+            
+            if mesh_simplify < 1.0:
+                try:
+                    target_faces = int(len(faces) * mesh_simplify)
+                    tri_mesh = tri_mesh.simplify_quadric_decimation(target_faces)
+                except Exception as e:
+                    print(f"Warning: Mesh simplification failed: {e}. Using original mesh.")
+            
+            mesh = Mesh.load_trimesh(given_mesh=tri_mesh)
+            mesh.auto_normal()
+            
+            return (mesh,)
+            
+        except Exception as e:
+            raise Exception(f"[StableGen_Trellis_Image_To_3D] 3D generation failed: {str(e)}")
+
+
+class StableGen_StableX_Process_Image:
+    """
+    Image processing pipeline using StableX model.
+    """
+
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("processed_image",)
+    FUNCTION = "run"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "stablex_pipe": ("DIFFUSERS_PIPE",),
+                "image": ("IMAGE",),
+                "processing_resolution": ("INT", {"default": 2048, "min": 64, "max": 4096, "step": 16}),
+                "controlnet_strength": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 10.0, "step": 0.01}),
+                "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}),
+            }
+        }
+
+    @torch.no_grad()
+    def run(self, stablex_pipe, image, processing_resolution=2048, controlnet_strength=1.0, seed=42):
+        if image.dim() == 4:
+            image = image.squeeze(0)
+        
+        image_tensor = image.permute(2, 0, 1).unsqueeze(0).to(DEVICE).to(torch.float16)
+
+        generator = torch.Generator(device=DEVICE).manual_seed(seed)
+
+        try:
+            pipe_out = stablex_pipe(
+                image_tensor,
+                controlnet_conditioning_scale=controlnet_strength,
+                processing_resolution=processing_resolution,
+                generator=generator,
+                output_type="pt",
+            )
+            
+            processed = (pipe_out.prediction.clip(-1, 1) + 1) / 2
+            out_tensor = processed.permute(0, 2, 3, 1).cpu().float()
+            
+            return (out_tensor,)
+            
+        except Exception as e:
+            raise Exception(f"[StableGen_StableX_Process_Image] Image processing failed: {str(e)}")
+# --- MV START ----
+class Load_MVAdapter_IG2MV_Pipeline:
+    """Loader pipeline for MV-Adapter (Image to Multi-View)"""
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("DIFFUSERS_PIPE",)
+    RETURN_NAMES = ("mvadapter_pipe",)
+    FUNCTION = "load"
+
+    CKPT_MVADAPTER_PATH = os.path.join(CKPT_DIFFUSERS_PATH, "huanngzh", "MV-Adapter")
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "base_model": (["stabilityai/stable-diffusion-xl-base-1.0"], 
+                             {"default": "stabilityai/stable-diffusion-xl-base-1.0"}),
+                "vae_model": (["madebyollin/sdxl-vae-fp16-fix", "None"], 
+                             {"default": "madebyollin/sdxl-vae-fp16-fix"}),
+                "adapter_path": (["huanngzh/mv-adapter"], {"default": "huanngzh/mv-adapter"}),
+                "scheduler": (["ddpm"], {"default": "ddpm"}),
+                "num_views": ("INT", {"default": 6, "min": 1, "max": 16}),
+                "use_fp16": ("BOOLEAN", {"default": True}),
+                "use_mmgp": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "lora_model": ("STRING", {"default": ""}),
+            }
+        }
+        
+    @classmethod
+    def load(cls, base_model, vae_model, adapter_path, scheduler, num_views, 
+            use_fp16, use_mmgp, lora_model=""):
+        
+        dtype = torch.float16 if use_fp16 else torch.float32
+        vae_model = None if vae_model == "None" else vae_model
+        lora_model = None if not lora_model else lora_model
+        
+        # Подготавливаем параметры для пайплайна
+        pipeline_kwargs = {
+            "base_model": base_model,
+            "vae_model": vae_model,
+            "lora_model": lora_model,
+            "adapter_path": adapter_path,
+            "scheduler": scheduler,
+            "num_views": num_views,
+            "device": DEVICE_STR,
+            "dtype": dtype,
+            "use_mmgp": use_mmgp,
+            "adapter_local_path": cls.CKPT_MVADAPTER_PATH
+        }
+        
+        pipe = mvadapter_prepare_pipeline(**pipeline_kwargs)
+        
+        print("MV-Adapter IG2MV pipeline loaded successfully")
+        return (pipe,)
+
+class MVAdapter_IG2MV:
+    """Generate multi-view images from single image and 3D mesh"""
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("multiview_images",)
+    FUNCTION = "run"
+
+    @classmethod  
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mvadapter_pipe": ("DIFFUSERS_PIPE",),
+                "mesh_path": ("STRING", {"default": ""}),
+                "reference_image": ("IMAGE",),
+                "prompt": ("STRING", {"default": "high quality", "multiline": True}),
+                "negative_prompt": ("STRING", {"default": "watermark, ugly, deformed, noisy, blurry, low contrast", "multiline": True}),
+                "num_inference_steps": ("INT", {"default": 50, "min": 1, "max": 200}),
+                "guidance_scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 20.0, "step": 0.1}),
+                "reference_conditioning_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
+                "height": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 8}),
+                "width": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 8}),
+                "seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
+                "remove_background": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
+            }
+        }
+
+    def run(self, mvadapter_pipe, mesh_path, reference_image, prompt, negative_prompt, 
+            num_inference_steps, guidance_scale, reference_conditioning_scale,
+            height, width, seed, remove_background, lora_scale=1.0):
+        
+        if isinstance(reference_image, torch.Tensor):
+            reference_images = torch_imgs_to_pils(reference_image)
+            reference_image = reference_images[0]
+        
+        if not mesh_path or not os.path.exists(mesh_path):
+            raise ValueError(f"Mesh path does not exist: {mesh_path}")
+        
+        num_views = 6 
+        images, pos_images, normal_images, processed_ref_image = mvadapter_run_pipeline(
+            pipe=mvadapter_pipe,
+            mesh_path=mesh_path,
+            num_views=num_views,
+            text=prompt,
+            image=reference_image,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            seed=seed,
+            reference_conditioning_scale=reference_conditioning_scale,
+            negative_prompt=negative_prompt,
+            lora_scale=lora_scale,
+            device=DEVICE_STR,
+        )
+        
+        return_images = pils_to_torch_imgs(images, device=DEVICE_STR)
+        return (return_images,)
+
+class Load_MVAdapter_TG2MV_Pipeline:
+    """Loader pipeline for MV-Adapter Text-Guided to Multi-View (TG2MV)"""
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("DIFFUSERS_PIPE",)
+    RETURN_NAMES = ("mvadapter_tg2mv_pipe",)
+    FUNCTION = "load"
+
+    CKPT_MVADAPTER_PATH = os.path.join(CKPT_DIFFUSERS_PATH, "huanngzh", "MV-Adapter")
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "base_model": (["stabilityai/stable-diffusion-xl-base-1.0"], 
+                             {"default": "stabilityai/stable-diffusion-xl-base-1.0"}),
+                "vae_model": (["madebyollin/sdxl-vae-fp16-fix", "None"], 
+                             {"default": "madebyollin/sdxl-vae-fp16-fix"}),
+                "adapter_path": (["huanngzh/mv-adapter"], {"default": "huanngzh/mv-adapter"}),
+                "scheduler": (["ddpm"], {"default": "ddpm"}),
+                "num_views": ("INT", {"default": 6, "min": 1, "max": 16}),
+                "use_fp16": ("BOOLEAN", {"default": True}),
+                "use_mmgp": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "lora_model": ("STRING", {"default": ""}),
+            }
+        }
+
+    @classmethod
+    def load(cls, base_model, vae_model, adapter_path, scheduler, num_views, 
+             use_fp16, use_mmgp, lora_model=""):
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        
+        dtype = torch.float16 if use_fp16 else torch.float32
+        vae_model = None if vae_model == "None" else vae_model
+        lora_model = None if not lora_model else lora_model
+        
+        pipeline_kwargs = {
+            "base_model": base_model,
+            "vae_model": vae_model,
+            "lora_model": lora_model,
+            "adapter_path": adapter_path,
+            "scheduler": scheduler,
+            "num_views": num_views,
+            "device": DEVICE_STR,
+            "dtype": dtype,
+            "use_mmgp": use_mmgp,
+            "adapter_local_path": cls.CKPT_MVADAPTER_PATH
+        }
+        
+        try:
+            pipe = mvadapter_prepare_tg2mv_pipeline(**pipeline_kwargs)
+            print("MV-Adapter TG2MV pipeline loaded successfully")
+            return (pipe,)
+            
+        except Exception as e:
+            raise e
+
+
+class MVAdapter_TG2MV:
+    """Generate multi-view images from text prompt using 3D mesh guidance"""
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("multiview_images",)
+    FUNCTION = "run"
+
+    @classmethod  
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mvadapter_tg2mv_pipe": ("DIFFUSERS_PIPE",),
+                "mesh_path": ("STRING", {"default": ""}),
+                "prompt": ("STRING", {"default": "a high quality 3D model", "multiline": True}),
+                "negative_prompt": ("STRING", {"default": "watermark, ugly, deformed, noisy, blurry, low contrast", "multiline": True}),
+                "num_views": ("INT", {"default": 6, "min": 1, "max": 16}),
+                "num_inference_steps": ("INT", {"default": 50, "min": 1, "max": 200}),
+                "guidance_scale": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 20.0, "step": 0.1}),
+                "height": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 8}),
+                "width": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 8}),
+                "seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
+            }
+        }
+
+    def run(self, mvadapter_tg2mv_pipe, mesh_path, prompt, negative_prompt, num_views,
+            num_inference_steps, guidance_scale, height, width, seed, lora_scale=1.0):
+        
+        if not mesh_path or not os.path.exists(mesh_path):
+            raise ValueError(f"Mesh path does not exist: {mesh_path}")
+        
+        images, pos_images, normal_images = mvadapter_run_tg2mv_pipeline(
+            pipe=mvadapter_tg2mv_pipe,
+            mesh_path=mesh_path,
+            num_views=num_views,
+            text=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            seed=seed,
+            negative_prompt=negative_prompt,
+            lora_scale=lora_scale,
+            device=DEVICE_STR,
+        )
+        
+        
+        return_images = pils_to_torch_imgs(images, device=DEVICE_STR)
+        return (return_images,)
+            
+class Load_MVAdapter_Texture_Pipeline:
+    """Load texture projection pipeline for MVAdapter"""
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("TEXTURE_PIPE",)
+    RETURN_NAMES = ("texture_pipeline",)
+    FUNCTION = "load"
+
+    TEXTURE_CKPT_DIR = os.path.join(CKPT_DIFFUSERS_PATH, "huanngzh/MV-Adapter")
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "upscaler_ckpt_name": ("STRING", {"default": "RealESRGAN_x2plus.pth"}),
+                "inpaint_ckpt_name": ("STRING", {"default": "big-lama.pt"}),
+                "use_mmgp": ("BOOLEAN", {"default": False}),
+                "auto_download": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    @classmethod
+    def load(cls, upscaler_ckpt_name, inpaint_ckpt_name, use_mmgp, auto_download):
+        
+        upscaler_ckpt_path = os.path.join(cls.TEXTURE_CKPT_DIR, upscaler_ckpt_name) if upscaler_ckpt_name.strip() else None
+        inpaint_ckpt_path = os.path.join(cls.TEXTURE_CKPT_DIR, inpaint_ckpt_name) if inpaint_ckpt_name.strip() else None
+        
+        if auto_download:
+            download_texture_checkpoints(cls.TEXTURE_CKPT_DIR, upscaler_ckpt_path, inpaint_ckpt_path)
+        
+        texture_pipe = mvadapter_prepare_texture_pipeline(
+            upscaler_ckpt_path=upscaler_ckpt_path,
+            inpaint_ckpt_path=inpaint_ckpt_path,
+            device=DEVICE_STR,
+            use_mmgp=use_mmgp,
+        )
+        
+        print("Texture pipeline loaded successfully")
+        return (texture_pipe,)
+    
+
+class MVAdapter_Texture_Projection:
+    """Project grid image onto 3D mesh using pre-loaded texture pipeline"""
+    CATEGORY = "Comfy3D/Algorithm"
+    RETURN_TYPES = ("STRING", "STRING")
+    RETURN_NAMES = ("shaded_model_path", "pbr_model_path") 
+    FUNCTION = "run"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "texture_pipeline": ("TEXTURE_PIPE",),
+                "grid_image": ("IMAGE",),
+                "mesh_path": ("STRING", {"default": ""}),
+                "save_dir": ("STRING", {"default": "./output"}),
+                "save_name": ("STRING", {"default": "textured_model"}),
+                "uv_size": ("INT", {"default": 4096, "min": 512, "max": 8192, "step": 256}),
+                "view_upscale": ("BOOLEAN", {"default": True}),
+                "inpaint_mode": (["none", "uv", "view"], {"default": "view"}),
+                "uv_unwarp": ("BOOLEAN", {"default": True}),
+                "preprocess_mesh": ("BOOLEAN", {"default": False}),
+                "move_to_center": ("BOOLEAN", {"default": False}),
+                "front_x": ("BOOLEAN", {"default": True}),
+                "create_pbr_model": ("BOOLEAN", {"default": True}),
+                "apply_dilate": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "camera_azimuth_deg": ("STRING", {"default": "0,90,180,270,180,180"}),
+                "camera_elevation_deg": ("STRING", {"default": "0,0,0,0,89.99,-89.99"}),
+                "camera_distance": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
+                "camera_ortho_scale": ("FLOAT", {"default": 1.1, "min": 0.1, "max": 5.0, "step": 0.1}),
+                "debug_mode": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    def run(self, texture_pipeline, grid_image, mesh_path, save_dir, save_name, 
+            uv_size, view_upscale, inpaint_mode, uv_unwarp, preprocess_mesh, 
+            move_to_center, front_x, create_pbr_model, apply_dilate,
+            camera_azimuth_deg="0,90,180,270,180,180",
+            camera_elevation_deg="0,0,0,0,89.99,-89.99", 
+            camera_distance=1.0, camera_ortho_scale=1.1, debug_mode=False):
+        
+        if isinstance(grid_image, torch.Tensor):
+            pil_grid = torch_imgs_to_pils(grid_image)[0]  # Get first (and only) image
+        else:
+            raise ValueError("grid_image must be torch.Tensor")
+        
+        if not mesh_path or not os.path.exists(mesh_path):
+            raise ValueError(f"Mesh file does not exist: {mesh_path}")
+        
+        save_dir = os.path.abspath(save_dir)
+        os.makedirs(save_dir, exist_ok=True)
+        
+        try:
+            azimuth_deg = [float(x.strip()) for x in camera_azimuth_deg.split(",")]
+            elevation_deg = [float(x.strip()) for x in camera_elevation_deg.split(",")]
+        except:
+            azimuth_deg = [0, 90, 180, 270, 180, 180]
+            elevation_deg = [0, 0, 0, 0, 89.99, -89.99]
+        
+        azimuth_deg_corrected = [x - 90 for x in azimuth_deg]
+        
+        temp_grid_path = os.path.join(save_dir, f"{save_name}_temp_grid.png")
+        pil_grid.save(temp_grid_path)
+        
+        try:
+            from .Gen_3D_Modules.MV_Adapter.mvadapter.pipelines.pipeline_texture import ModProcessConfig
+            
+            rgb_process_config = ModProcessConfig(
+                view_upscale=view_upscale,
+                inpaint_mode=inpaint_mode
+            )
+            
+            base_color_process_config = ModProcessConfig(
+                view_upscale=view_upscale,
+                inpaint_mode=inpaint_mode
+            )
+            
+            texture_args = {
+                "mesh_path": mesh_path,
+                "save_dir": save_dir,
+                "save_name": save_name,
+                "move_to_center": move_to_center,
+                "front_x": front_x,
+                "uv_unwarp": uv_unwarp,
+                "preprocess_mesh": preprocess_mesh,
+                "uv_size": uv_size,
+                "rgb_path": temp_grid_path,
+                "rgb_process_config": rgb_process_config,
+                "camera_elevation_deg": elevation_deg,
+                "camera_azimuth_deg": azimuth_deg_corrected,
+                "camera_distance": camera_distance,
+                "camera_ortho_scale": camera_ortho_scale,
+                "debug_mode": debug_mode,
+                "apply_dilate": apply_dilate,
+            }
+            
+            if create_pbr_model:
+                texture_args.update({
+                    "base_color_path": temp_grid_path,
+                    "base_color_process_config": base_color_process_config,
+                })
+            
+            output = texture_pipeline(**texture_args)
+            
+            if os.path.exists(temp_grid_path):
+                os.remove(temp_grid_path)
+            
+            shaded_path = os.path.abspath(output.shaded_model_save_path) if output.shaded_model_save_path else ""
+            pbr_path = os.path.abspath(output.pbr_model_save_path) if output.pbr_model_save_path else ""
+            
+            print(f"Texturing from grid completed:")
+            print(f"  Shaded model: {shaded_path}")
+            print(f"  PBR model: {pbr_path}")
+                
+            return (shaded_path, pbr_path)
+            
+        except Exception as e:
+            if os.path.exists(temp_grid_path):
+                os.remove(temp_grid_path)
+            raise e
+
diff --git a/pyproject.toml b/pyproject.toml
index 5471130..12277ae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "ComfyUI-3D-Pack"
 description = "Make ComfyUI generates 3D assets as good & convenient as it generates image/video!"
-version = "0.1.4"
+version = "0.1.5"
 license = { file = "LICENSE" }
 dynamic = ["dependencies"]
 [tool.setuptools.dynamic]
diff --git a/requirements.txt b/requirements.txt
index 4f1293e..8b6c13d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,7 @@
 # build tools
 cmake
 ninja
+pccm
 
 # web tools
 PyGithub
@@ -15,13 +16,17 @@ opencv-python
 pillow
 nerfacc>=0.5.3
 slangtorch
+cumm==0.7.11 # for spconv
+
 git+https://github.com/EasternJournalist/utils3d.git#egg=utils3d
+git+https://github.com/ashawkey/kiuikit.git#egg=kiuikit
 
 # for use ML models
 diffusers>=0.26.1
 transformers>=4.36.2
 safetensors
 open_clip_torch
+mmgp
 
 # for training differentiable tensors
 torchmetrics
@@ -38,6 +43,7 @@ matplotlib
 
 # for process, import & export mesh 
 trimesh
+fast-simplification
 plyfile
 pygltflib
 xatlas
@@ -48,6 +54,7 @@ libigl
 pyvista
 pymeshfix
 igraph
+diso
 
 # configs & extra
 torchtyping>=0.1.5