diff --git a/app.py b/app.py
index 05a8083..2a22a55 100644
--- a/app.py
+++ b/app.py
@@ -1,23 +1,72 @@
-import gradio as gr
-from gradio_litmodel3d import LitModel3D
-
 import os
-import shutil
-from typing import *
+import sys 
+import subprocess
 import torch
 import numpy as np
 import imageio
+import shutil 
+import gradio as gr
+from enum import Enum 
+from typing import *
+from functools import partial 
 from easydict import EasyDict as edict
 from PIL import Image
+from gradio_litmodel3d import LitModel3D
 from trellis.pipelines import TrellisImageTo3DPipeline
 from trellis.representations import Gaussian, MeshExtractResult
-from trellis.utils import render_utils, postprocessing_utils
-
+from trellis.utils import render_utils, postprocessing_utils, general_utils
 
 MAX_SEED = np.iinfo(np.int32).max
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMP_DIR, exist_ok=True)
 
+pipeline = None
+
+# Default sampling steps for SparseStructure SLAT 
+DEFAULT_SS_SAMPLE_STEPS = 12
+DEFAULT_SLAT_SAMPLE_STEPS = 12
+DETAIL_VAR_SLAT_SAMPLE_STEPS = 25 
+
+# Voxelizer (for detail variation pipeline)
+VOXELIZER = "cuda_voxelizer"
+VOVEL_RESOLUTION = 64 
+VOXELIZER_CMD = "{} -f {} -s {} -o binvox"
+
+class DemoMode(Enum):
+    SINGLE_IMAGE = 0
+    MULTI_IMAGE = 1 
+    DETAIL_VARIATION = 2 
+
+def check_voxelizer_exists(executable):
+    """ Install the CUDA Voxelizer according to https://github.com/Forceflow/cuda_voxelizer.
+    """
+    try:
+        subprocess.check_output([executable])
+    except subprocess.CalledProcessError:
+        print("Can not find the voxelizer!!! Install it according to https://github.com/Forceflow/cuda_voxelizer.")
+        sys.exit(-1)
+
+def voxelize_mesh(inpath: str):
+    """ 
+    Voxelize given mesh(OBJ) 
+
+    Args:
+        inpath (str): input path of the mesh model 
+
+    Returns:
+        str: output path 
+    """
+    outpath = os.path.join(os.path.dirname(inpath), f"{os.path.basename(inpath)}_{VOVEL_RESOLUTION}.binvox")
+    if os.path.exists(outpath):
+        return outpath 
+    
+    check_voxelizer_exists(VOXELIZER)
+    cmd = VOXELIZER_CMD.format(VOXELIZER, inpath, VOVEL_RESOLUTION)
+    voxelizer_ans = subprocess.run(cmd, capture_output=True, shell=True)
+    if voxelizer_ans.returncode != 0 or not os.path.exists(outpath):
+        print("voxelizer fails with error:")
+        print(voxelizer_ans.stderr)
+    return outpath 
 
 def start_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
@@ -39,6 +88,11 @@ def preprocess_image(image: Image.Image) -> Image.Image:
     Returns:
         Image.Image: The preprocessed image.
     """
+    global pipeline 
+    if pipeline is None:
+        pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+        pipeline.cuda()
+
     processed_image = pipeline.preprocess_image(image)
     return processed_image
 
@@ -57,6 +111,27 @@ def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image
     processed_images = [pipeline.preprocess_image(image) for image in images]
     return processed_images
 
+def preprocess_mesh(inpath: str) -> str: 
+    """
+    Preprocess a mesh for PREVIEW (colorize and rotation)
+    
+    Args:
+        inpath (str): input path of the mesh 
+
+    Returns:
+        str: path of the processed mesh 
+    """
+    import trimesh 
+    inmesh = trimesh.load(inpath)
+    vertices = np.array(inmesh.vertices)
+    # Normalize 
+    colors = (vertices - vertices.min(axis=0, keepdims=True)) / (vertices.max(axis=0, keepdims=True) - vertices.min(axis=0, keepdims=True))
+    colors = (colors * 255).astype(np.uint8)
+    vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) @ np.array([[-1, 0., 0.], [0., 1., 0.], [0., 0., -1.]])
+    newmesh = trimesh.Trimesh(vertices, inmesh.faces, visual=trimesh.visual.color.ColorVisuals(vertex_colors=colors))
+    outpath = os.path.join(os.path.dirname(inpath), os.path.splitext(os.path.basename(inpath))[0] + "_colorized.obj")
+    newmesh.export(outpath)
+    return outpath  
 
 def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
     return {
@@ -108,7 +183,9 @@ def get_seed(randomize_seed: bool, seed: int) -> int:
 def image_to_3d(
     image: Image.Image,
     multiimages: List[Tuple[Image.Image, str]],
-    is_multiimage: bool,
+    image_detail_var: Image.Image, 
+    raw_mesh: str, 
+    demo_mode: DemoMode,
     seed: int,
     ss_guidance_strength: float,
     ss_sampling_steps: int,
@@ -123,7 +200,9 @@ def image_to_3d(
     Args:
         image (Image.Image): The input image.
         multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
-        is_multiimage (bool): Whether is in multi-image mode.
+        image_detail_var (Image.Image): The input image used for detailed variation.
+        raw_mesh (str): raw mesh (ONLY required for Detail Variation mode).
+        demo_mode (Demo): Mode of current run (SingleImageTo3D / MultiImageTo3D / DetailVariation).
         seed (int): The random seed.
         ss_guidance_strength (float): The guidance strength for sparse structure generation.
         ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
@@ -134,9 +213,10 @@ def image_to_3d(
     Returns:
         dict: The information of the generated 3D model.
         str: The path to the video of the 3D model.
-    """
+    """    
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    if not is_multiimage:
+    
+    if demo_mode == DemoMode.SINGLE_IMAGE:
         outputs = pipeline.run(
             image,
             seed=seed,
@@ -151,7 +231,7 @@ def image_to_3d(
                 "cfg_strength": slat_guidance_strength,
             },
         )
-    else:
+    elif demo_mode == DemoMode.MULTI_IMAGE:
         outputs = pipeline.run_multi_image(
             [image[0] for image in multiimages],
             seed=seed,
@@ -167,21 +247,37 @@ def image_to_3d(
             },
             mode=multiimage_algo,
         )
+    else:
+        import trimesh 
+        binary_voxel = trimesh.load(voxelize_mesh(raw_mesh)).matrix 
+        outputs = pipeline.run_detail_variation(
+            binary_voxel, 
+            image_detail_var,
+            seed=seed, 
+            formats=["gaussian", "mesh"],
+            preprocess_image=False,
+            slat_sampler_params={
+                "steps": slat_sampling_steps,
+                "cfg_strength": slat_guidance_strength,
+            },
+        )
+    torch.cuda.empty_cache()
     video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
     video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
     video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
     video_path = os.path.join(user_dir, 'sample.mp4')
     imageio.mimsave(video_path, video, fps=15)
     state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
-    torch.cuda.empty_cache()
     return state, video_path
 
 
-def extract_glb(
-    state: dict,
-    mesh_simplify: float,
-    texture_size: int,
-    req: gr.Request,
+def extract_mesh(
+    state: dict, 
+    get_srgb_texture: bool, 
+    texture_bake_mode: Literal['opt', 'fast'], 
+    mesh_simplify: float, 
+    texture_size: int, 
+    req: gr.Request, 
 ) -> Tuple[str, str]:
     """
     Extract a GLB file from the 3D model.
@@ -196,11 +292,20 @@ def extract_glb(
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, mesh = unpack_state(state)
-    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
-    glb_path = os.path.join(user_dir, 'sample.glb')
-    glb.export(glb_path)
+    extracted_mesh = postprocessing_utils.to_trimesh(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, texture_bake_mode=texture_bake_mode, get_srgb_texture=get_srgb_texture, verbose=True)
+    glb_path = os.path.join(user_dir, "sample.glb")
+    extracted_mesh.export(glb_path)
+    # also export the obj version 
+    obj_dir = os.path.join(user_dir, "sample_obj")
+    if os.path.exists(obj_dir):
+        shutil.rmtree(obj_dir)
+    obj_zip_path = os.path.join(user_dir, "sample_obj.zip")
+    os.makedirs(obj_dir, exist_ok=True)
+    extracted_mesh.export(os.path.join(obj_dir, f"sample.obj"))
+    # compress obj and its dependencies 
+    general_utils.zip_directory(obj_dir, obj_zip_path)
     torch.cuda.empty_cache()
-    return glb_path, glb_path
+    return glb_path, glb_path, obj_zip_path
 
 
 def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
@@ -254,7 +359,7 @@ def split_image(image: Image.Image) -> List[Image.Image]:
     gr.Markdown("""
     ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
     * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
-    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
+    * If you find the generated 3D asset satisfactory, click "Extract Mesh" to extract the GLB/OBJ file and download it.
     """)
     
     with gr.Row():
@@ -269,6 +374,16 @@ def split_image(image: Image.Image) -> List[Image.Image]:
                         
                         *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
                     """)
+                with gr.Tab(label="Detail Variation", id=2) as detail_var_input_tab:
+                    with gr.Row():
+                        mesh_prompt = gr.Model3D(label="Input Raw Mesh(Z-UP)", height=300, display_mode="Solid", clear_color=[1, 1, 1, 1])
+                        mesh_prompt_view = gr.Model3D(label="Preview Input Mesh", height=300, display_mode="Solid", clear_color=[1, 1, 1, 1])
+                        image_prompt_detail_var = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
+                    gr.Markdown("""
+                        Input a mesh w/o texture and a reference image. 
+                        
+                        *NOTE: this is an experimental feature.
+                    """)
             
             with gr.Accordion(label="Generation Settings", open=False):
                 seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
@@ -276,35 +391,38 @@ def split_image(image: Image.Image) -> List[Image.Image]:
                 gr.Markdown("Stage 1: Sparse Structure Generation")
                 with gr.Row():
                     ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
-                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=DEFAULT_SS_SAMPLE_STEPS, step=1)
                 gr.Markdown("Stage 2: Structured Latent Generation")
                 with gr.Row():
                     slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
-                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=DEFAULT_SLAT_SAMPLE_STEPS, step=1)
                 multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
 
             generate_btn = gr.Button("Generate")
             
-            with gr.Accordion(label="GLB Extraction Settings", open=False):
-                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
+            with gr.Accordion(label="Mesh Extraction Settings", open=True):
+                get_srgb_texture = gr.Checkbox(label="Get sRGB Texture", value=False)
+                texture_bake_mode = gr.Radio(["opt", "fast"], label="Texture Bake Mode", value='opt')
+                mesh_simplify = gr.Slider(0.5, 0.99, label="Simplify", value=0.95, step=0.01)
                 texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
             
             with gr.Row():
-                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
+                extract_mesh_btn = gr.Button("Extract Mesh", interactive=False)
                 extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
             gr.Markdown("""
                         *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
                         """)
 
         with gr.Column():
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
-            
+            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=250)
+            model_output = LitModel3D(label="Extracted Mesh / Gaussian", exposure=20.0, height=250)
             with gr.Row():
                 download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
+                download_obj = gr.DownloadButton(label="Download OBJ", interactive=False)
                 download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
-    
-    is_multiimage = gr.State(False)
+            
+    # single_image / multi_image / detail variation 
+    demo_mode = gr.State(DemoMode.SINGLE_IMAGE)
     output_buf = gr.State()
 
     # Example images at the bottom of the page
@@ -329,18 +447,48 @@ def split_image(image: Image.Image) -> List[Image.Image]:
             run_on_click=True,
             examples_per_page=8,
         )
+    with gr.Row(visible=False) as detail_var_example: 
+        examples_mesh = gr.Examples(
+            examples=[
+                f'assets/example_mesh/{mesh}'
+                for mesh in os.listdir("assets/example_mesh") if mesh.endswith(".obj")
+            ],
+            fn=preprocess_mesh,
+            inputs=[mesh_prompt],
+            outputs=[mesh_prompt_view],
+            run_on_click=True,
+            examples_per_page=64,
+            label="Example Meshes"
+        )
+          
+        examples_image = gr.Examples(
+            examples=[
+                f'assets/example_image/{image}'
+                for image in os.listdir("assets/example_image")
+            ],
+            inputs=[image_prompt_detail_var],
+            fn=preprocess_image,
+            outputs=[image_prompt_detail_var],
+            run_on_click=True,
+            examples_per_page=64,
+            label="Example Images"
+        )
 
     # Handlers
     demo.load(start_session)
     demo.unload(end_session)
     
     single_image_input_tab.select(
-        lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
+        lambda: tuple([DemoMode.SINGLE_IMAGE, DEFAULT_SLAT_SAMPLE_STEPS, gr.Row.update(visible=True), gr.Row.update(visible=False), gr.Row.update(visible=False)]),
+        outputs=[demo_mode, slat_sampling_steps, single_image_example, multiimage_example, detail_var_example]
     )
     multiimage_input_tab.select(
-        lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
+        lambda: tuple([DemoMode.MULTI_IMAGE, DEFAULT_SLAT_SAMPLE_STEPS, gr.Row.update(visible=False), gr.Row.update(visible=True), gr.Row.update(visible=False)]),
+        outputs=[demo_mode, slat_sampling_steps, single_image_example, multiimage_example, detail_var_example]
+    )
+    detail_var_input_tab.select(
+        lambda: tuple([DemoMode.DETAIL_VARIATION, DETAIL_VAR_SLAT_SAMPLE_STEPS, gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=True)]),
+        outputs=[demo_mode, slat_sampling_steps, single_image_example, multiimage_example, detail_var_example] 
     )
     
     image_prompt.upload(
@@ -353,6 +501,16 @@ def split_image(image: Image.Image) -> List[Image.Image]:
         inputs=[multiimage_prompt],
         outputs=[multiimage_prompt],
     )
+    image_prompt_detail_var.upload(
+        preprocess_images, 
+        inputs=[image_prompt_detail_var],
+        outputs=[image_prompt_detail_var],
+    )
+    mesh_prompt.upload(
+        preprocess_mesh, 
+        inputs=[mesh_prompt], 
+        outputs=[mesh_prompt_view]
+    )
 
     generate_btn.click(
         get_seed,
@@ -360,25 +518,25 @@ def split_image(image: Image.Image) -> List[Image.Image]:
         outputs=[seed],
     ).then(
         image_to_3d,
-        inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
+        inputs=[image_prompt, multiimage_prompt, image_prompt_detail_var, mesh_prompt, demo_mode, seed, ss_guidance_strength,  ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
         outputs=[output_buf, video_output],
     ).then(
         lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
+        outputs=[extract_mesh_btn, extract_gs_btn],
     )
 
     video_output.clear(
         lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
+        outputs=[extract_mesh_btn, extract_gs_btn],
     )
-
-    extract_glb_btn.click(
-        extract_glb,
-        inputs=[output_buf, mesh_simplify, texture_size],
-        outputs=[model_output, download_glb],
+    
+    extract_mesh_btn.click(
+        extract_mesh,
+        inputs=[output_buf, get_srgb_texture, texture_bake_mode, mesh_simplify, texture_size],
+        outputs=[model_output, download_glb, download_obj],
     ).then(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_glb],
+        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
+        outputs=[download_glb, download_obj],
     )
     
     extract_gs_btn.click(
@@ -391,13 +549,12 @@ def split_image(image: Image.Image) -> List[Image.Image]:
     )
 
     model_output.clear(
-        lambda: gr.Button(interactive=False),
-        outputs=[download_glb],
+        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
+        outputs=[download_glb, download_obj],
     )
     
+    
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    pipeline.cuda()
-    demo.launch()
+    demo.launch(debug=True, server_name='0.0.0.0', server_port=6006)
diff --git a/assets/example_mesh/typical_creature_dragon.obj b/assets/example_mesh/typical_creature_dragon.obj
new file mode 100644
index 0000000..a81e053
--- /dev/null
+++ b/assets/example_mesh/typical_creature_dragon.obj
@@ -0,0 +1,37610 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
+v 0.21940368 -0.04814186 0.03333973
+v 0.21504712 -0.05114838 0.03612969
+v 0.21169704 -0.05429883 0.04086906
+v 0.04463576 -0.12201367 -0.12620781
+v 0.04702861 -0.13154297 -0.12993544
+v 0.04743931 -0.14039214 -0.12583083
+v -0.14414725 -0.20245644 -0.41992667
+v -0.07807291 -0.15331958 -0.41931531
+v -0.07627457 -0.20692648 -0.42002577
+v -0.13676453 -0.13581200 -0.41842929
+v -0.14529891 -0.23977590 -0.42010480
+v -0.10852606 -0.23532431 -0.42021447
+v -0.07796057 -0.22904904 -0.42001957
+v -0.07927924 -0.13930000 -0.41857895
+v -0.15414967 -0.14380750 -0.41791603
+v -0.06096318 -0.20288482 -0.41909564
+v -0.06202042 -0.16092142 -0.41872174
+v -0.15638806 -0.19758843 -0.41884288
+v -0.11048864 -0.12610400 -0.41729176
+v -0.14875948 -0.12435624 -0.41751802
+v -0.15298429 -0.13811997 -0.41884080
+v -0.10034557 -0.24356969 -0.41934025
+v -0.05890476 -0.13411273 -0.41624847
+v -0.08319955 -0.11924861 -0.41566601
+v -0.12702429 -0.24054031 -0.41926613
+v -0.06373812 -0.22359957 -0.41834468
+v -0.15624276 -0.12331595 -0.41642779
+v -0.06673072 -0.12404869 -0.41488719
+v -0.07597514 -0.23997138 -0.41784307
+v -0.11816147 -0.11885440 -0.41526401
+v -0.14900689 -0.24052918 -0.41909471
+v -0.14422709 -0.24583362 -0.42013472
+v -0.16315883 -0.24960019 -0.41959476
+v -0.15662760 -0.25992593 -0.42072347
+v -0.14292687 -0.25419345 -0.41955334
+v -0.16678737 -0.25975448 -0.41925147
+v -0.16323984 -0.14589667 -0.41911033
+v -0.18557177 -0.13412115 -0.42322263
+v -0.16510011 -0.12402730 -0.41872537
+v -0.18837772 -0.14358354 -0.42187130
+v -0.19893721 -0.13531677 -0.42171624
+v -0.19425766 -0.12571703 -0.42080098
+v -0.18331699 -0.12300370 -0.42054844
+v -0.11486425 -0.24388146 -0.41876024
+v -0.05369132 -0.14879809 -0.41542065
+v -0.15206385 -0.11585958 -0.41468477
+v -0.12531611 -0.24707098 -0.42110991
+v -0.11742140 -0.26393652 -0.42219329
+v -0.12811501 -0.27928206 -0.42435211
+v -0.13427883 -0.25520355 -0.42161271
+v -0.13798425 -0.27402803 -0.42328304
+v -0.13354120 -0.29242793 -0.42257822
+v -0.12128897 -0.28203657 -0.42270139
+v -0.12706046 -0.29201850 -0.42163575
+v -0.13812181 -0.24473964 -0.41733202
+v -0.14731228 -0.26049328 -0.41866004
+v -0.18388599 -0.14920883 -0.41950026
+v -0.15916380 -0.26451191 -0.41880316
+v -0.08216255 -0.24999550 -0.41386870
+v -0.07707576 -0.25076574 -0.41298011
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diff --git a/assets/example_mesh/typical_creature_elephant.obj b/assets/example_mesh/typical_creature_elephant.obj
new file mode 100644
index 0000000..cbab48b
--- /dev/null
+++ b/assets/example_mesh/typical_creature_elephant.obj
@@ -0,0 +1,40766 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
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diff --git a/assets/example_mesh/typical_creature_furry.obj b/assets/example_mesh/typical_creature_furry.obj
new file mode 100644
index 0000000..797e256
--- /dev/null
+++ b/assets/example_mesh/typical_creature_furry.obj
@@ -0,0 +1,32477 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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diff --git a/assets/example_mesh/typical_creature_quadruped.obj b/assets/example_mesh/typical_creature_quadruped.obj
new file mode 100644
index 0000000..3b547f1
--- /dev/null
+++ b/assets/example_mesh/typical_creature_quadruped.obj
@@ -0,0 +1,77621 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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diff --git a/assets/example_mesh/typical_creature_robot_crab.obj b/assets/example_mesh/typical_creature_robot_crab.obj
new file mode 100644
index 0000000..02f35bb
--- /dev/null
+++ b/assets/example_mesh/typical_creature_robot_crab.obj
@@ -0,0 +1,67374 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
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diff --git a/assets/example_mesh/typical_creature_robot_dinosour.obj b/assets/example_mesh/typical_creature_robot_dinosour.obj
new file mode 100644
index 0000000..1f9205c
--- /dev/null
+++ b/assets/example_mesh/typical_creature_robot_dinosour.obj
@@ -0,0 +1,26204 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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+v -0.12615617 -0.22396976 -0.29475498
+v -0.13257973 -0.21968724 -0.29431301
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+v 0.13985862 -0.30586535 -0.09582550
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diff --git a/assets/example_mesh/typical_creature_rock_monster.obj b/assets/example_mesh/typical_creature_rock_monster.obj
new file mode 100644
index 0000000..7891579
--- /dev/null
+++ b/assets/example_mesh/typical_creature_rock_monster.obj
@@ -0,0 +1,60040 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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diff --git a/assets/example_mesh/typical_humanoid_block_robot.obj b/assets/example_mesh/typical_humanoid_block_robot.obj
new file mode 100644
index 0000000..bfe08e3
--- /dev/null
+++ b/assets/example_mesh/typical_humanoid_block_robot.obj
@@ -0,0 +1,52316 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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diff --git a/assets/example_mesh/typical_humanoid_dragonborn.obj b/assets/example_mesh/typical_humanoid_dragonborn.obj
new file mode 100644
index 0000000..d37a012
--- /dev/null
+++ b/assets/example_mesh/typical_humanoid_dragonborn.obj
@@ -0,0 +1,40586 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
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+
diff --git a/assets/example_mesh/typical_humanoid_dwarf.obj b/assets/example_mesh/typical_humanoid_dwarf.obj
new file mode 100644
index 0000000..fb3ae40
--- /dev/null
+++ b/assets/example_mesh/typical_humanoid_dwarf.obj
@@ -0,0 +1,84297 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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diff --git a/assets/example_mesh/typical_humanoid_goblin.obj b/assets/example_mesh/typical_humanoid_goblin.obj
new file mode 100644
index 0000000..88de5ee
--- /dev/null
+++ b/assets/example_mesh/typical_humanoid_goblin.obj
@@ -0,0 +1,41758 @@
+# https://github.com/mikedh/trimesh
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diff --git a/assets/example_mesh/typical_humanoid_mech.obj b/assets/example_mesh/typical_humanoid_mech.obj
new file mode 100644
index 0000000..b7d29ae
--- /dev/null
+++ b/assets/example_mesh/typical_humanoid_mech.obj
@@ -0,0 +1,60009 @@
+# https://github.com/mikedh/trimesh
+mtllib material.mtl
+usemtl material_0
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+f 3244/3244 3223/3223 3176/3176
+
diff --git a/example_detail_variation.py b/example_detail_variation.py
new file mode 100644
index 0000000..1a8ffe8
--- /dev/null
+++ b/example_detail_variation.py
@@ -0,0 +1,104 @@
+import os
+import sys 
+# os.environ['ATTN_BACKEND'] = 'xformers'   # Can be 'flash-attn' or 'xformers', default is 'flash-attn'
+os.environ['SPCONV_ALGO'] = 'native'        # Can be 'native' or 'auto', default is 'auto'.
+                                            # 'auto' is faster but will do benchmarking at the beginning.
+                                            # Recommended to set to 'native' if run only once.
+
+import subprocess
+import imageio
+import trimesh 
+import torch
+from PIL import Image
+from trellis.pipelines import TrellisImageTo3DPipeline
+from trellis.utils import render_utils, postprocessing_utils
+
+"""
+Image-version detail variation (Sec3.4 of the paper)
+1. Voxelize a GIVEN mesh into the form of Sparse Structure.
+    A CUDA VOXELIZER is required. Install according to https://github.com/Forceflow/cuda_voxelizer.
+2. Run ONLY the second stage with the image prompt.
+"""
+
+VOXELIZER = "cuda_voxelizer"
+VOVEL_RESOLUTION = 64 
+VOXELIZER_CMD = "{} -f {} -s {} -o binvox"
+
+def _check_voxelizer_exists(executable):
+    try:
+        subprocess.check_output([executable])
+    except subprocess.CalledProcessError:
+        print("Can not find the voxelizer!!!")
+        sys.exit(-1)
+
+def _voxelize_mesh(inpath: str):
+    outpath = os.path.join(os.path.dirname(inpath), f"{os.path.basename(inpath)}_{VOVEL_RESOLUTION}.binvox")
+    if os.path.exists(outpath):
+        return outpath 
+    
+    _check_voxelizer_exists(VOXELIZER)
+    cmd = VOXELIZER_CMD.format(VOXELIZER, inpath, VOVEL_RESOLUTION)
+    voxelizer_ans = subprocess.run(cmd, capture_output=True, shell=True)
+    if voxelizer_ans.returncode != 0 or not os.path.exists(outpath):
+        print("voxelizer fails with error:")
+        print(voxelizer_ans.stderr)
+    return outpath 
+
+# Load a pipeline from a model folder or a Hugging Face model hub.
+pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+pipeline.cuda()
+
+# Test image path and saving directory
+saveroot = "results/texgen"
+
+example_mesh_image_pairs = [
+    ("assets/example_mesh/typical_creature_dragon.obj", "assets/example_image/typical_creature_elephant.png"), 
+    ("assets/example_mesh/typical_creature_dragon.obj", "assets/example_image/typical_creature_furry.png"), 
+    ("assets/example_mesh/typical_creature_dragon.obj", "assets/example_image/typical_creature_robot_dinosour.png"), 
+    ("assets/example_mesh/typical_creature_dragon.obj", "assets/example_image/typical_creature_robot_crab.png"), 
+    ("assets/example_mesh/typical_humanoid_block_robot.obj", "assets/example_image/typical_building_mushroom.png"), 
+    ("assets/example_mesh/typical_humanoid_block_robot.obj", "assets/example_image/typical_humanoid_mech.png")
+]
+
+for mesh_image_pair in example_mesh_image_pairs:
+    mesh_path, image_path = mesh_image_pair
+    instance_name = f"{os.path.splitext(os.path.basename(mesh_path))[0]}+{os.path.splitext(os.path.basename(image_path))[0]}"
+    savedir = os.path.join(saveroot, instance_name)
+    os.makedirs(savedir, exist_ok=True)
+
+    # Load the image 
+    image = Image.open(image_path)
+
+    binary_voxel = trimesh.load(_voxelize_mesh(mesh_path)).matrix 
+
+    # Run the pipeline
+    outputs = pipeline.run_texgen(
+        binary_voxel, 
+        image,
+        seed=1,
+        # more steps, larger cfg 
+        slat_sampler_params={
+                "steps": 35,
+                "cfg_strength": 6.0,
+            },
+    )
+
+    torch.cuda.empty_cache()
+    # Render the outputs
+    video = render_utils.render_video(outputs['gaussian'][0])['color']
+    imageio.mimsave(os.path.join(savedir, f"{instance_name}_gs.mp4"), video, fps=30)
+    video = render_utils.render_video(outputs['radiance_field'][0])['color']
+    imageio.mimsave(os.path.join(savedir, f"{instance_name}_rf.mp4"), video, fps=30)
+    video = render_utils.render_video(outputs['mesh'][0])['normal']
+    imageio.mimsave(os.path.join(savedir, f"{instance_name}_mesh.mp4"), video, fps=30)
+
+    # GLB files can be extracted from the outputs
+    glb = postprocessing_utils.to_trimesh(
+        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
+        debug=False, 
+        verbose=True
+    )
diff --git a/trellis/pipelines/trellis_image_to_3d.py b/trellis/pipelines/trellis_image_to_3d.py
index f781e34..ec82fac 100644
--- a/trellis/pipelines/trellis_image_to_3d.py
+++ b/trellis/pipelines/trellis_image_to_3d.py
@@ -1,9 +1,10 @@
+import os 
 from typing import *
 from contextlib import contextmanager
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import numpy as np
+import numpy as np 
 from tqdm import tqdm
 from easydict import EasyDict as edict
 from torchvision import transforms
@@ -81,6 +82,19 @@ def _init_image_cond_model(self, name: str):
             transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
         ])
         self.image_cond_model_transform = transform
+        
+    def preprocess_voxel(self, binary_voxel: np.ndarray, voxel_res: int = 64) -> torch.Tensor: 
+        """ 
+        Preprocess(read / voxelize) the given 3D object.
+        """
+        assert all([s == voxel_res for s in binary_voxel.shape]), "Input voxels have incompatible resolution {}".format(binary_voxel.shape)
+        # Active voxels (N_p x 3)
+        x, y, z = np.nonzero(binary_voxel)
+        values_sum = x * voxel_res * voxel_res + y * voxel_res + z 
+        active_voxels = np.stack([x, y, z], axis=1)[np.argsort(values_sum)]
+        active_voxels = np.concatenate((np.zeros((len(active_voxels), 1), dtype=np.int32), active_voxels), axis=1)
+        # Pad with the batch dimension 
+        return torch.from_numpy(active_voxels).int().cuda()
 
     def preprocess_image(self, input: Image.Image) -> Image.Image:
         """
@@ -253,11 +267,51 @@ def sample_slat(
         slat = slat * std + mean
         
         return slat
+    
+    @torch.no_grad()
+    def run_detail_variation(
+        self,
+        binary_voxel: np.ndarray, 
+        images: Union[Image.Image, 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,
+    ) -> dict:
+        """
+        Run the texture generation(2nd stage) pipeline.
+
+        Args:
+            binary_voxel (np.ndarray): The input binary voxel.
+            image (Image.Image or a list of Image.Image): The image prompt(s).
+            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.
+        """
+        conds = []
+        if isinstance(images, Image.Image):
+            images = [images]
+        # Get condition for each image prompt and take average 
+        for image in images:
+            if preprocess_image:
+                image = self.preprocess_image(image)
+            conds.append(self.get_cond([image]))
+        cond = {
+            key: torch.stack([item[key] for item in conds], dim=0).mean(dim=0) for key in conds[0].keys()
+        }
+            
+        torch.manual_seed(seed)
+        coords = self.preprocess_voxel(binary_voxel)
+        slat = self.sample_slat(cond, coords, slat_sampler_params)
+        return self.decode_slat(slat, formats)
 
     @torch.no_grad()
     def run(
         self,
-        image: Image.Image,
+        images: Union[Image.Image, List[Image.Image]],
         num_samples: int = 1,
         seed: int = 42,
         sparse_structure_sampler_params: dict = {},
@@ -269,15 +323,24 @@ def run(
         Run the pipeline.
 
         Args:
-            image (Image.Image): The image prompt.
+            image (Image.Image or a list of Image.Image): The image prompt(s).
             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)
-        cond = self.get_cond([image])
+        conds = []
+        if isinstance(images, Image.Image):
+            images = [images]
+        # Get condition for each image prompt and take average 
+        for image in images:
+            if preprocess_image:
+                image = self.preprocess_image(image)
+            conds.append(self.get_cond([image]))
+        cond = {
+            key: torch.stack([item[key] for item in conds], dim=0).mean(dim=0) for key in conds[0].keys()
+        }
+            
         torch.manual_seed(seed)
         coords = self.sample_sparse_structure(cond, num_samples, sparse_structure_sampler_params)
         slat = self.sample_slat(cond, coords, slat_sampler_params)
diff --git a/trellis/representations/gaussian/gaussian_model.py b/trellis/representations/gaussian/gaussian_model.py
index 54ba16f..338fa23 100755
--- a/trellis/representations/gaussian/gaussian_model.py
+++ b/trellis/representations/gaussian/gaussian_model.py
@@ -110,7 +110,7 @@ def from_features(self, features):
     def from_opacity(self, opacities):
         self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias
 
-    def construct_list_of_attributes(self):
+    def construct_list_of_attributes(self, mode='all'):        
         l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
         # All channels except the 3 DC
         for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
@@ -121,7 +121,7 @@ def construct_list_of_attributes(self):
         for i in range(self._rotation.shape[1]):
             l.append('rot_{}'.format(i))
         return l
-        
+
     def save_ply(self, path, transform=[[1, 0, 0], [0, 0, -1], [0, 1, 0]]):
         xyz = self.get_xyz.detach().cpu().numpy()
         normals = np.zeros_like(xyz)
diff --git a/trellis/utils/general_utils.py b/trellis/utils/general_utils.py
index 3b454d9..cb0abf4 100755
--- a/trellis/utils/general_utils.py
+++ b/trellis/utils/general_utils.py
@@ -1,3 +1,5 @@
+import os 
+import zipfile 
 import numpy as np
 import cv2
 import torch
@@ -185,3 +187,18 @@ def indent(s, n=4):
         lines[i] = ' ' * n + lines[i]
     return '\n'.join(lines)
 
+def zip_directory(directory_path, zip_file_path):
+    """
+    compress directory into a zipfile 
+    """
+    if not os.path.isdir(directory_path):
+        raise FileNotFoundError(f"Directory {directory_path} not found.")
+
+    with zipfile.ZipFile(zip_file_path, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+        for root, dirs, files in os.walk(directory_path):
+            for dir_name in dirs:
+                dir_path = os.path.join(root, dir_name)
+                zip_file.write(dir_path, os.path.relpath(dir_path, directory_path))
+            for file_name in files:
+                file_path = os.path.join(root, file_name)
+                zip_file.write(file_path, os.path.relpath(file_path, directory_path))
diff --git a/trellis/utils/postprocessing_utils.py b/trellis/utils/postprocessing_utils.py
index 0a8d9fb..51737e2 100644
--- a/trellis/utils/postprocessing_utils.py
+++ b/trellis/utils/postprocessing_utils.py
@@ -17,6 +17,26 @@
 from ..renderers import GaussianRenderer
 from ..representations import Strivec, Gaussian, MeshExtractResult
 
+def _rgb_to_srgb(f: torch.Tensor) -> torch.Tensor:
+    """ 
+    convert a tensor, in any form / dimension, from rgb space to srgb space 
+    Args:
+        f (torch.Tensor): input tensor
+
+    """
+    return torch.where(f <= 0.0031308, f * 12.92, torch.pow(torch.clamp(f, 0.0031308), 1.0/2.4)*1.055 - 0.055)
+
+def rgb_to_srgb_image(f: torch.Tensor) -> torch.Tensor:
+    """ 
+    convert an image tensor from rgb space to srgb space 
+    Args:
+        f (torch.Tensor): input tensor
+
+    """
+    assert f.shape[-1] == 3 or f.shape[-1] == 4
+    out = torch.cat((_rgb_to_srgb(f[..., 0:3]), f[..., 3:4]), dim=-1) if f.shape[-1] == 4 else _rgb_to_srgb(f)
+    assert out.shape[0] == f.shape[0] and out.shape[1] == f.shape[1] and out.shape[2] == f.shape[2]
+    return out
 
 @torch.no_grad()
 def _fill_holes(
@@ -151,7 +171,7 @@ def _fill_holes(
             for i, edge_cc in enumerate(cc_new_boundary_edge_cc):
                 _e1 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 0]] - cc_new_boundary_edges_cc_center[i]
                 _e2 = verts[edges[cc_new_boundary_edge_indices[edge_cc]][:, 1]] - cc_new_boundary_edges_cc_center[i]
-                cc_new_boundary_edges_cc_area.append(torch.norm(torch.cross(_e1, _e2, dim=-1), dim=1).sum() * 0.5)
+                cc_new_boundary_edges_cc_area.append(torch.norm(torch.cross(_e1, _e2, dim=-1), dim=1).sum().item() * 0.5)
             if debug:
                 cutting_edges.append(cc_new_boundary_edge_indices)
                 tqdm.write(f'Area of the cutting loop: {cc_new_boundary_edges_cc_area}')
@@ -169,11 +189,11 @@ def _fill_holes(
         vis_colors[remove_face_indices.cpu().numpy()] = [255, 0, 255]
         if len(valid_remove_cc) > 0:
             vis_colors[remove_face_indices[torch.cat(valid_remove_cc)].cpu().numpy()] = [255, 0, 0]
-        utils3d.io.write_ply('dbg_dual.ply', face_v, edges=vis_dual_edges, vertex_colors=vis_colors)
+        utils3d.io.write_ply('debug/dbg_dual.ply', face_v, edges=vis_dual_edges, vertex_colors=vis_colors)
         
         vis_verts = verts.cpu().numpy()
         vis_edges = edges[torch.cat(cutting_edges)].cpu().numpy()
-        utils3d.io.write_ply('dbg_cut.ply', vis_verts, edges=vis_edges)
+        utils3d.io.write_ply('debug/dbg_cut.ply', vis_verts, edges=vis_edges)
         
     
     if len(valid_remove_cc) > 0:
@@ -286,6 +306,7 @@ def bake_texture(
     far: float = 10.0,
     mode: Literal['fast', 'opt'] = 'opt',
     lambda_tv: float = 1e-2,
+    srgb_space: bool = False, 
     verbose: bool = False,
 ):
     """
@@ -337,10 +358,13 @@ def bake_texture(
         mask = texture_weights > 0
         texture[mask] /= texture_weights[mask][:, None]
         texture = np.clip(texture.reshape(texture_size, texture_size, 3).cpu().numpy() * 255, 0, 255).astype(np.uint8)
+        if srgb_space:
+            # convert the texture from rgb space to srgb 
+            texture = rgb_to_srgb_image(texture) 
 
         # inpaint
         mask = (texture_weights == 0).cpu().numpy().astype(np.uint8).reshape(texture_size, texture_size)
-        texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
+        texture_inpainted = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
 
     elif mode == 'opt':
         rastctx = utils3d.torch.RastContext(backend='cuda')
@@ -348,7 +372,7 @@ def bake_texture(
         masks = [m.flip(0) for m in masks]
         _uv = []
         _uv_dr = []
-        for observation, view, projection in tqdm(zip(observations, views, projections), total=len(views), disable=not verbose, desc='Texture baking (opt): UV'):
+        for observation, view, projection in tqdm(zip(observations, views, projections), total=len(views), disable=not verbose, desc='Texture baking rasterization (opt): UV'):
             with torch.no_grad():
                 rast = utils3d.torch.rasterize_triangle_faces(
                     rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection
@@ -385,24 +409,31 @@ def tv_loss(texture):
                 optimizer.param_groups[0]['lr'] = cosine_anealing(optimizer, step, total_steps, 1e-2, 1e-5)
                 pbar.set_postfix({'loss': loss.item()})
                 pbar.update()
+                
+        if srgb_space:
+            # convert the texture from rgb space to srgb 
+            texture = rgb_to_srgb_image(texture) 
+                    
         texture = np.clip(texture[0].flip(0).detach().cpu().numpy() * 255, 0, 255).astype(np.uint8)
         mask = 1 - utils3d.torch.rasterize_triangle_faces(
             rastctx, (uvs * 2 - 1)[None], faces, texture_size, texture_size
         )['mask'][0].detach().cpu().numpy().astype(np.uint8)
-        texture = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
+        texture_inpainted = cv2.inpaint(texture, mask, 3, cv2.INPAINT_TELEA)
     else:
         raise ValueError(f'Unknown mode: {mode}')
 
-    return texture
+    return texture, texture_inpainted
 
 
-def to_glb(
+def to_trimesh(
     app_rep: Union[Strivec, Gaussian],
     mesh: MeshExtractResult,
     simplify: float = 0.95,
     fill_holes: bool = True,
     fill_holes_max_size: float = 0.04,
     texture_size: int = 1024,
+    texture_bake_mode: Literal['fast', 'opt'] = 'opt',
+    get_srgb_texture: bool = False, 
     debug: bool = False,
     verbose: bool = True,
 ) -> trimesh.Trimesh:
@@ -419,12 +450,12 @@ def to_glb(
         debug (bool): Whether to print debug information.
         verbose (bool): Whether to print progress.
     """
-    vertices = mesh.vertices.cpu().numpy()
-    faces = mesh.faces.cpu().numpy()
+    vertices_raw = mesh.vertices.cpu().numpy()
+    faces_raw = mesh.faces.cpu().numpy()
     
     # mesh postprocess
     vertices, faces = postprocess_mesh(
-        vertices, faces,
+        vertices_raw, faces_raw,
         simplify=simplify > 0,
         simplify_ratio=simplify,
         fill_holes=fill_holes,
@@ -444,15 +475,19 @@ def to_glb(
     masks = [np.any(observation > 0, axis=-1) for observation in observations]
     extrinsics = [extrinsics[i].cpu().numpy() for i in range(len(extrinsics))]
     intrinsics = [intrinsics[i].cpu().numpy() for i in range(len(intrinsics))]
-    texture = bake_texture(
+    # texture_raw indicates the texture before inpainting 
+    texture_raw, texture = bake_texture(
         vertices, faces, uvs,
         observations, masks, extrinsics, intrinsics,
-        texture_size=texture_size, mode='opt',
+        texture_size=texture_size, mode=texture_bake_mode,
+        srgb_space=get_srgb_texture,
         lambda_tv=0.01,
         verbose=verbose
     )
+    texture_raw = Image.fromarray(texture_raw)
     texture = Image.fromarray(texture)
 
+    mesh = trimesh.Trimesh(vertices, faces, visual=trimesh.visual.TextureVisuals(uv=uvs, image=texture))
     # rotate mesh (from z-up to y-up)
     vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
     material = trimesh.visual.material.PBRMaterial(
@@ -460,9 +495,20 @@ def to_glb(
         baseColorTexture=texture,
         baseColorFactor=np.array([255, 255, 255, 255], dtype=np.uint8)
     )
-    mesh = trimesh.Trimesh(vertices, faces, visual=trimesh.visual.TextureVisuals(uv=uvs, material=material))
-    return mesh
-
+    mesh_yup = trimesh.Trimesh(vertices, faces, visual=trimesh.visual.TextureVisuals(uv=uvs, material=material))
+    if debug:
+        return {
+            "mesh_raw": trimesh.Trimesh(vertices_raw, faces_raw), 
+            "mesh": mesh, 
+            "mesh_yup": mesh_yup, 
+            "texture_raw": texture_raw, 
+            "texture": texture, 
+            "observations": observations,
+            "intrinsics": intrinsics,
+            "extrinsics": extrinsics,
+        }
+    
+    return mesh_yup
 
 def simplify_gs(
     gs: Gaussian,
diff --git a/trellis/utils/render_utils.py b/trellis/utils/render_utils.py
index 8187c84..6fd8e65 100644
--- a/trellis/utils/render_utils.py
+++ b/trellis/utils/render_utils.py
@@ -49,6 +49,7 @@ def render_frames(sample, extrinsics, intrinsics, options={}, colors_overwrite=N
         renderer.rendering_options.bg_color = options.get('bg_color', (0, 0, 0))
         renderer.rendering_options.ssaa = options.get('ssaa', 4)
         renderer.pipe.primitive = sample.primitive
+        primitive_type = "Octree"
     elif isinstance(sample, Gaussian):
         renderer = GaussianRenderer()
         renderer.rendering_options.resolution = options.get('resolution', 512)
@@ -58,17 +59,19 @@ def render_frames(sample, extrinsics, intrinsics, options={}, colors_overwrite=N
         renderer.rendering_options.ssaa = options.get('ssaa', 1)
         renderer.pipe.kernel_size = kwargs.get('kernel_size', 0.1)
         renderer.pipe.use_mip_gaussian = True
+        primitive_type = "Gaussian"
     elif isinstance(sample, MeshExtractResult):
         renderer = MeshRenderer()
         renderer.rendering_options.resolution = options.get('resolution', 512)
         renderer.rendering_options.near = options.get('near', 1)
         renderer.rendering_options.far = options.get('far', 100)
         renderer.rendering_options.ssaa = options.get('ssaa', 4)
+        primitive_type = "Mesh"
     else:
         raise ValueError(f'Unsupported sample type: {type(sample)}')
     
     rets = {}
-    for j, (extr, intr) in tqdm(enumerate(zip(extrinsics, intrinsics)), desc='Rendering', disable=not verbose):
+    for j, (extr, intr) in tqdm(enumerate(zip(extrinsics, intrinsics)), desc=f'Rendering {primitive_type}', total=len(extrinsics), disable=not verbose):
         if not isinstance(sample, MeshExtractResult):
             res = renderer.render(sample, extr, intr, colors_overwrite=colors_overwrite)
             if 'color' not in rets: rets['color'] = []