Update gradio_app.py

gradio_app.py

@@ -1,339 +1,334 @@
-import spaces
-import argparse
-import os
-import json
-import torch
-import sys
-import time
-import importlib
-import numpy as np
-from omegaconf import OmegaConf
-from huggingface_hub import hf_hub_download
-
-from collections import OrderedDict
-import trimesh
-import gradio as gr
-from typing import Any
-
-proj_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(os.path.join(proj_dir))
-
-import tempfile
-
-from apps.utils import *
-
-_TITLE = '''CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner'''
-_DESCRIPTION = '''
-<div>
-Select or upload a image, then just click 'Generate'. 
-<br>
-By mimicking the artist/craftsman modeling workflow, we propose CraftsMan (aka 匠心) that uses 3D Latent Set Diffusion Model that directly generate coarse meshes,
-then a multi-view normal enhanced image generation model is used to refine the mesh.
-We provide the coarse 3D diffusion part here. 
-<br>
-If you found CraftsMan is helpful, please help to ⭐ the <a href='https://github.com/wyysf-98/CraftsMan/' target='_blank'>Github Repo</a>. Thanks!
-<a style="display:inline-block; margin-left: .5em" href='https://github.com/wyysf-98/CraftsMan/'><img src='https://img.shields.io/github/stars/wyysf-98/CraftsMan?style=social' /></a>
-<br>
-*If you have your own multi-view images, you can directly upload it.
-</div>
-'''
-_CITE_ = r"""
----
-📝 **Citation**
-If you find our work useful for your research or applications, please cite using this bibtex:
-```bibtex
-@article{craftsman,
-author    = {Weiyu Li and Jiarui Liu and Rui Chen and Yixun Liang and Xuelin Chen and Ping Tan and Xiaoxiao Long},
-title     = {CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner},
-journal   = {arxiv:xxx},
-year      = {2024},
-}
-```
-🤗 **Acknowledgements**
-We use <a href='https://github.com/wjakob/instant-meshes' target='_blank'>Instant Meshes</a> to remesh the generated mesh to a lower face count, thanks to the authors for the great work.
-📋 **License**
-CraftsMan is under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html), so any downstream solution and products (including cloud services) that include CraftsMan code or a trained model (both pretrained or custom trained) inside it should be open-sourced to comply with the AGPL conditions. If you have any questions about the usage of CraftsMan, please contact us first.
-📧 **Contact**
-If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
-"""
-from apps.third_party.CRM.pipelines import TwoStagePipeline
-from apps.third_party.LGM.pipeline_mvdream import MVDreamPipeline
-
-
-model = None
-cached_dir = None
-stage1_config = OmegaConf.load(f"{parent_dir}/apps/third_party/CRM/configs/nf7_v3_SNR_rd_size_stroke.yaml").config
-stage1_sampler_config = stage1_config.sampler
-stage1_model_config = stage1_config.models
-stage1_model_config.resume = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth", repo_type="model")
-stage1_model_config.config = f"{parent_dir}/apps/third_party/CRM/" + stage1_model_config.config
-crm_pipeline = None
-
-sys.path.append(f"apps/third_party/LGM")
-imgaedream_pipeline = None
-
-generator = None
-
-@spaces.GPU
-def gen_mvimg(
-    mvimg_model, image, seed, guidance_scale, step, text, neg_text, elevation, 
-):
-    if seed == 0:
-        seed = np.random.randint(1, 65535)
-
-    if mvimg_model == "CRM":
-        global crm_pipeline
-        crm_pipeline.set_seed(seed)
-        mv_imgs = crm_pipeline(
-            image, 
-            scale=guidance_scale, 
-            step=step
-        )["stage1_images"]
-        return mv_imgs[5], mv_imgs[3], mv_imgs[2], mv_imgs[0]
-    
-    elif mvimg_model == "ImageDream":
-        global imagedream_pipeline, generator
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])
-        mv_imgs = imagedream_pipeline(
-            text, 
-            image, 
-            negative_prompt=neg_text, 
-            guidance_scale=guidance_scale,  
-            num_inference_steps=step, 
-            elevation=elevation,
-            generator=generator.manual_seed(seed),
-        )
-        return mv_imgs[1], mv_imgs[2], mv_imgs[3], mv_imgs[0]
-    
-
-@spaces.GPU
-def image2mesh(view_front: np.ndarray, 
-               view_right: np.ndarray, 
-               view_back: np.ndarray, 
-               view_left: np.ndarray,
-               more: bool = False,
-               scheluder_name: str ="DDIMScheduler",
-               guidance_scale: int = 7.5,
-               seed: int = 4,
-               octree_depth: int = 7):
-    
-    sample_inputs = {
-        "mvimages": [[
-            Image.fromarray(view_front), 
-            Image.fromarray(view_right), 
-            Image.fromarray(view_back), 
-            Image.fromarray(view_left)
-        ]]
-    }
-
-    global model
-    latents = model.sample(
-        sample_inputs,
-        sample_times=1,
-        guidance_scale=guidance_scale,
-        return_intermediates=False,
-        seed=seed
-       
-    )[0]
-    
-    # decode the latents to mesh
-    box_v = 1.1
-    mesh_outputs, _ = model.shape_model.extract_geometry(
-        latents,
-        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
-        octree_depth=octree_depth
-    )
-    assert len(mesh_outputs) == 1, "Only support single mesh output for gradio demo"
-    mesh = trimesh.Trimesh(mesh_outputs[0][0], mesh_outputs[0][1])
-    # filepath = f"{cached_dir}/{time.time()}.obj"
-    filepath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
-    mesh.export(filepath, include_normals=True)
-
-    if 'Remesh' in more:
-        remeshed_filepath = tempfile.NamedTemporaryFile(suffix=f"_remeshed.obj", delete=False).name
-        print("Remeshing with Instant Meshes...")
-        # target_face_count = int(len(mesh.faces)/10)
-        target_face_count = 2000
-        command = f"{proj_dir}/apps/third_party/InstantMeshes {filepath} -f {target_face_count} -o {remeshed_filepath}"
-        os.system(command)
-        filepath = remeshed_filepath
-        # filepath = filepath.replace('.obj', '_remeshed.obj')
-    
-    return filepath
-
-if __name__=="__main__":
-    parser = argparse.ArgumentParser()
-    # parser.add_argument("--model_path", type=str, required=True, help="Path to the object file",)
-    parser.add_argument("--cached_dir", type=str, default="./gradio_cached_dir")
-    parser.add_argument("--device", type=int, default=0)
-    args = parser.parse_args()
-
-    cached_dir = args.cached_dir
-    os.makedirs(args.cached_dir, exist_ok=True)
-    device = torch.device(f"cuda:{args.device}" if torch.cuda.is_available() else "cpu")
-    print(f"using device: {device}")
-
-    # for multi-view images generation
-    background_choice = OrderedDict({ 
-        "Alpha as Mask": "Alpha as Mask",
-        "Auto Remove Background": "Auto Remove Background",
-        "Original Image": "Original Image",        
-    })
-    mvimg_model_config_list = ["CRM", "ImageDream"]
-    crm_pipeline = TwoStagePipeline(
-                        stage1_model_config,
-                        stage1_sampler_config,
-                        device=device,
-                        dtype=torch.float16
-                    )
-    imagedream_pipeline = MVDreamPipeline.from_pretrained(
-        "ashawkey/imagedream-ipmv-diffusers", # remote weights
-        torch_dtype=torch.float16,
-        trust_remote_code=True,
-    )
-    generator = torch.Generator(device)
-
-
-    # for 3D latent set diffusion
-    ckpt_path = "./ckpts/image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/model.ckpt"
-    config_path = "./ckpts/image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/config.yaml"
-    # ckpt_path = hf_hub_download(repo_id="wyysf/CraftsMan", filename="image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/model.ckpt", repo_type="model")
-    # config_path = hf_hub_download(repo_id="wyysf/CraftsMan", filename="image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/config.yaml", repo_type="model")
-    scheluder_dict = OrderedDict({ 
-        "DDIMScheduler": 'diffusers.schedulers.DDIMScheduler',
-        # "DPMSolverMultistepScheduler": 'diffusers.schedulers.DPMSolverMultistepScheduler', # not support yet
-        # "UniPCMultistepScheduler": 'diffusers.schedulers.UniPCMultistepScheduler', # not support yet
-    })
-    
-    # main GUI
-    custom_theme = gr.themes.Soft(primary_hue="blue").set(
-                    button_secondary_background_fill="*neutral_100",
-                    button_secondary_background_fill_hover="*neutral_200")
-    custom_css = '''#disp_image {
-        text-align: center; /* Horizontally center the content */
-    }'''
-    
-    with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo:
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown('# ' + _TITLE)
-        gr.Markdown(_DESCRIPTION)
-
-        with gr.Row():
-            with gr.Column(scale=2):
-                with gr.Column():
-                    # input image
-                    with gr.Row():
-                        image_input = gr.Image(
-                            label="Image Input",
-                            image_mode="RGBA",
-                            sources="upload",
-                            type="pil",
-                        )
-                run_btn = gr.Button('Generate', variant='primary', interactive=True)
-
-                with gr.Row():
-                    gr.Markdown('''Try a different <b>seed and MV Model</b> for better results. Good Luck :)''')
-                with gr.Row():
-                    seed = gr.Number(0, label='Seed', show_label=True)
-                    mvimg_model = gr.Dropdown(value="CRM", label="MV Image Model", choices=list(mvimg_model_config_list))
-                    more = gr.CheckboxGroup(["Remesh", "Symmetry(TBD)"], label="More", show_label=False)
-                with gr.Row():
-                    # input prompt
-                    text = gr.Textbox(label="Prompt (Opt.)", info="only works for ImageDream")
-
-                with gr.Accordion('Advanced options', open=False):
-                    # negative prompt
-                    neg_text = gr.Textbox(label="Negative Prompt", value='ugly, blurry, pixelated obscure, unnatural colors, poor lighting, dull, unclear, cropped, lowres, low quality, artifacts, duplicate')
-                    # elevation
-                    elevation = gr.Slider(label="elevation", minimum=-90, maximum=90, step=1, value=0)
-
-                with gr.Row():
-                    gr.Examples(
-                        examples=[os.path.join("./apps/examples", i) for i in os.listdir("./apps/examples")],
-                        inputs=[image_input],
-                        examples_per_page=8
-                    )
-
-            with gr.Column(scale=4):
-                with gr.Row():
-                    output_model_obj = gr.Model3D(
-                        label="Output Model (OBJ Format)",
-                        camera_position=(90.0, 90.0, 3.5),
-                        interactive=False,
-                    )
-                with gr.Row():
-                    gr.Markdown('''*please note that the model is fliped due to the gradio viewer, please download the obj file and you will get the correct orientation.''')
-                    
-                with gr.Row():
-                    view_front = gr.Image(label="Front", interactive=True, show_label=True)
-                    view_right = gr.Image(label="Right", interactive=True, show_label=True)
-                    view_back = gr.Image(label="Back", interactive=True, show_label=True)
-                    view_left = gr.Image(label="Left", interactive=True, show_label=True)
-                    
-                with gr.Accordion('Advanced options', open=False):
-                    with gr.Row(equal_height=True):
-                        run_mv_btn = gr.Button('Only Generate 2D', interactive=True)
-                        run_3d_btn = gr.Button('Only Generate 3D', interactive=True)
-
-                with gr.Accordion('Advanced options (2D)', open=False):
-                    with gr.Row():
-                        foreground_ratio = gr.Slider(
-                                label="Foreground Ratio",
-                                minimum=0.5,
-                                maximum=1.0,
-                                value=1.0,
-                                step=0.05,
-                            )
-                        
-                    with gr.Row():
-                        background_choice = gr.Dropdown(label="Backgroud Choice", value="Auto Remove Background",choices=list(background_choice.keys()))
-                        rmbg_type = gr.Dropdown(label="Backgroud Remove Type", value="rembg",choices=['sam', "rembg"])
-                        backgroud_color = gr.ColorPicker(label="Background Color", value="#FFFFFF", interactive=True)
-                        # backgroud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=True)
-                        
-                    with gr.Row():
-                        mvimg_guidance_scale = gr.Number(value=4.0, minimum=3, maximum=10, label="2D Guidance Scale")
-                        mvimg_steps = gr.Number(value=30, minimum=20, maximum=100, label="2D Sample Steps")
-            
-                with gr.Accordion('Advanced options (3D)', open=False):
-                    with gr.Row():
-                        guidance_scale = gr.Number(label="3D Guidance Scale", value=7.5, minimum=3.0, maximum=10.0)
-                        steps = gr.Number(value=50, minimum=20, maximum=100, label="3D Sample Steps")
-                        
-                    with gr.Row():
-                        scheduler = gr.Dropdown(label="scheluder", value="DDIMScheduler",choices=list(scheluder_dict.keys()))
-                        octree_depth = gr.Slider(label="Octree Depth", value=7, minimum=4, maximum=8, step=1)
-                    
-        gr.Markdown(_CITE_)
-
-        outputs = [output_model_obj]
-        rmbg = RMBG(device)
-        
-        model = load_model(ckpt_path, config_path, device)
-
-        run_btn.click(fn=check_input_image, inputs=[image_input]
-                    ).success(
-                            fn=rmbg.run, 
-                            inputs=[rmbg_type, image_input, foreground_ratio, background_choice, backgroud_color],
-                            outputs=[image_input]
-                    ).success(
-                            fn=gen_mvimg,
-                            inputs=[mvimg_model, image_input, seed, mvimg_guidance_scale, mvimg_steps, text, neg_text, elevation],
-                            outputs=[view_front, view_right, view_back, view_left]
-                    ).success(
-                            fn=image2mesh, 
-                            inputs=[view_front, view_right, view_back, view_left, more, scheduler, guidance_scale, seed, octree_depth],
-                            outputs=outputs, 
-                            api_name="generate_img2obj")
-        run_mv_btn.click(fn=gen_mvimg,
-                        inputs=[mvimg_model, image_input, seed, mvimg_guidance_scale, mvimg_steps, text, neg_text, elevation],
-                        outputs=[view_front, view_right, view_back, view_left]
-        )
-        run_3d_btn.click(fn=image2mesh, 
-                        inputs=[view_front, view_right, view_back, view_left, more, scheduler, guidance_scale, seed, octree_depth],
-                        outputs=outputs, 
-                        api_name="generate_img2obj")
-        
+import spaces
+import argparse
+import os
+import json
+import torch
+import sys
+import time
+import importlib
+import numpy as np
+from omegaconf import OmegaConf
+from huggingface_hub import hf_hub_download
+
+from collections import OrderedDict
+import trimesh
+import gradio as gr
+from typing import Any
+
+proj_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(proj_dir))
+
+import tempfile
+
+from apps.utils import *
+
+_TITLE = '''CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner'''
+_DESCRIPTION = '''
+<div>
+Select or upload a image, then just click 'Generate'. 
+<br>
+By mimicking the artist/craftsman modeling workflow, we propose CraftsMan (aka 匠心) that uses 3D Latent Set Diffusion Model that directly generate coarse meshes,
+then a multi-view normal enhanced image generation model is used to refine the mesh.
+We provide the coarse 3D diffusion part here. 
+<br>
+If you found CraftsMan is helpful, please help to ⭐ the <a href='https://github.com/wyysf-98/CraftsMan/' target='_blank'>Github Repo</a>. Thanks!
+<a style="display:inline-block; margin-left: .5em" href='https://github.com/wyysf-98/CraftsMan/'><img src='https://img.shields.io/github/stars/wyysf-98/CraftsMan?style=social' /></a>
+<br>
+*If you have your own multi-view images, you can directly upload it.
+</div>
+'''
+_CITE_ = r"""
+---
+📝 **Citation**
+If you find our work useful for your research or applications, please cite using this bibtex:
+```bibtex
+@article{craftsman,
+author    = {Weiyu Li and Jiarui Liu and Rui Chen and Yixun Liang and Xuelin Chen and Ping Tan and Xiaoxiao Long},
+title     = {CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner},
+journal   = {arxiv:xxx},
+year      = {2024},
+}
+```
+🤗 **Acknowledgements**
+We use <a href='https://github.com/wjakob/instant-meshes' target='_blank'>Instant Meshes</a> to remesh the generated mesh to a lower face count, thanks to the authors for the great work.
+📋 **License**
+CraftsMan is under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html), so any downstream solution and products (including cloud services) that include CraftsMan code or a trained model (both pretrained or custom trained) inside it should be open-sourced to comply with the AGPL conditions. If you have any questions about the usage of CraftsMan, please contact us first.
+📧 **Contact**
+If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
+"""
+from apps.third_party.CRM.pipelines import TwoStagePipeline
+from apps.third_party.LGM.pipeline_mvdream import MVDreamPipeline
+
+
+model = None
+cached_dir = None
+stage1_config = OmegaConf.load(f"{parent_dir}/apps/third_party/CRM/configs/nf7_v3_SNR_rd_size_stroke.yaml").config
+stage1_sampler_config = stage1_config.sampler
+stage1_model_config = stage1_config.models
+stage1_model_config.resume = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth", repo_type="model")
+stage1_model_config.config = f"{parent_dir}/apps/third_party/CRM/" + stage1_model_config.config
+crm_pipeline = None
+
+sys.path.append(f"apps/third_party/LGM")
+imgaedream_pipeline = None
+
+@spaces.GPU
+def gen_mvimg(
+    mvimg_model, image, seed, guidance_scale, step, text, neg_text, elevation, 
+):
+    if seed == 0:
+        seed = np.random.randint(1, 65535)
+
+    if mvimg_model == "CRM":
+        global crm_pipeline
+        crm_pipeline.set_seed(seed)
+        mv_imgs = crm_pipeline(
+            image, 
+            scale=guidance_scale, 
+            step=step
+        )["stage1_images"]
+        return mv_imgs[5], mv_imgs[3], mv_imgs[2], mv_imgs[0]
+    
+    elif mvimg_model == "ImageDream":
+        global imagedream_pipeline, generator
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])
+        mv_imgs = imagedream_pipeline(
+            text, 
+            image, 
+            negative_prompt=neg_text, 
+            guidance_scale=guidance_scale,  
+            num_inference_steps=step, 
+            elevation=elevation,
+        )
+        return mv_imgs[1], mv_imgs[2], mv_imgs[3], mv_imgs[0]
+    
+
+@spaces.GPU
+def image2mesh(view_front: np.ndarray, 
+               view_right: np.ndarray, 
+               view_back: np.ndarray, 
+               view_left: np.ndarray,
+               more: bool = False,
+               scheluder_name: str ="DDIMScheduler",
+               guidance_scale: int = 7.5,
+               seed: int = 4,
+               octree_depth: int = 7):
+    
+    sample_inputs = {
+        "mvimages": [[
+            Image.fromarray(view_front), 
+            Image.fromarray(view_right), 
+            Image.fromarray(view_back), 
+            Image.fromarray(view_left)
+        ]]
+    }
+
+    global model
+    latents = model.sample(
+        sample_inputs,
+        sample_times=1,
+        guidance_scale=guidance_scale,
+        return_intermediates=False,
+        seed=seed
+       
+    )[0]
+    
+    # decode the latents to mesh
+    box_v = 1.1
+    mesh_outputs, _ = model.shape_model.extract_geometry(
+        latents,
+        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
+        octree_depth=octree_depth
+    )
+    assert len(mesh_outputs) == 1, "Only support single mesh output for gradio demo"
+    mesh = trimesh.Trimesh(mesh_outputs[0][0], mesh_outputs[0][1])
+    # filepath = f"{cached_dir}/{time.time()}.obj"
+    filepath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
+    mesh.export(filepath, include_normals=True)
+
+    if 'Remesh' in more:
+        remeshed_filepath = tempfile.NamedTemporaryFile(suffix=f"_remeshed.obj", delete=False).name
+        print("Remeshing with Instant Meshes...")
+        # target_face_count = int(len(mesh.faces)/10)
+        target_face_count = 2000
+        command = f"{proj_dir}/apps/third_party/InstantMeshes {filepath} -f {target_face_count} -o {remeshed_filepath}"
+        os.system(command)
+        filepath = remeshed_filepath
+        # filepath = filepath.replace('.obj', '_remeshed.obj')
+    
+    return filepath
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser()
+    # parser.add_argument("--model_path", type=str, required=True, help="Path to the object file",)
+    parser.add_argument("--cached_dir", type=str, default="./gradio_cached_dir")
+    parser.add_argument("--device", type=int, default=0)
+    args = parser.parse_args()
+
+    cached_dir = args.cached_dir
+    os.makedirs(args.cached_dir, exist_ok=True)
+    device = torch.device(f"cuda:{args.device}" if torch.cuda.is_available() else "cpu")
+    print(f"using device: {device}")
+
+    # for multi-view images generation
+    background_choice = OrderedDict({ 
+        "Alpha as Mask": "Alpha as Mask",
+        "Auto Remove Background": "Auto Remove Background",
+        "Original Image": "Original Image",        
+    })
+    mvimg_model_config_list = ["CRM", "ImageDream"]
+    crm_pipeline = TwoStagePipeline(
+                        stage1_model_config,
+                        stage1_sampler_config,
+                        device=device,
+                        dtype=torch.float16
+                    )
+    imagedream_pipeline = MVDreamPipeline.from_pretrained(
+        "ashawkey/imagedream-ipmv-diffusers", # remote weights
+        torch_dtype=torch.float16,
+        trust_remote_code=True,
+    )
+
+    # for 3D latent set diffusion
+    ckpt_path = "./ckpts/image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/model.ckpt"
+    config_path = "./ckpts/image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/config.yaml"
+    # ckpt_path = hf_hub_download(repo_id="wyysf/CraftsMan", filename="image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/model.ckpt", repo_type="model")
+    # config_path = hf_hub_download(repo_id="wyysf/CraftsMan", filename="image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/config.yaml", repo_type="model")
+    scheluder_dict = OrderedDict({ 
+        "DDIMScheduler": 'diffusers.schedulers.DDIMScheduler',
+        # "DPMSolverMultistepScheduler": 'diffusers.schedulers.DPMSolverMultistepScheduler', # not support yet
+        # "UniPCMultistepScheduler": 'diffusers.schedulers.UniPCMultistepScheduler', # not support yet
+    })
+    
+    # main GUI
+    custom_theme = gr.themes.Soft(primary_hue="blue").set(
+                    button_secondary_background_fill="*neutral_100",
+                    button_secondary_background_fill_hover="*neutral_200")
+    custom_css = '''#disp_image {
+        text-align: center; /* Horizontally center the content */
+    }'''
+    
+    with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                with gr.Column():
+                    # input image
+                    with gr.Row():
+                        image_input = gr.Image(
+                            label="Image Input",
+                            image_mode="RGBA",
+                            sources="upload",
+                            type="pil",
+                        )
+                run_btn = gr.Button('Generate', variant='primary', interactive=True)
+
+                with gr.Row():
+                    gr.Markdown('''Try a different <b>seed and MV Model</b> for better results. Good Luck :)''')
+                with gr.Row():
+                    seed = gr.Number(0, label='Seed', show_label=True)
+                    mvimg_model = gr.Dropdown(value="CRM", label="MV Image Model", choices=list(mvimg_model_config_list))
+                    more = gr.CheckboxGroup(["Remesh", "Symmetry(TBD)"], label="More", show_label=False)
+                with gr.Row():
+                    # input prompt
+                    text = gr.Textbox(label="Prompt (Opt.)", info="only works for ImageDream")
+
+                with gr.Accordion('Advanced options', open=False):
+                    # negative prompt
+                    neg_text = gr.Textbox(label="Negative Prompt", value='ugly, blurry, pixelated obscure, unnatural colors, poor lighting, dull, unclear, cropped, lowres, low quality, artifacts, duplicate')
+                    # elevation
+                    elevation = gr.Slider(label="elevation", minimum=-90, maximum=90, step=1, value=0)
+
+                with gr.Row():
+                    gr.Examples(
+                        examples=[os.path.join("./apps/examples", i) for i in os.listdir("./apps/examples")],
+                        inputs=[image_input],
+                        examples_per_page=8
+                    )
+
+            with gr.Column(scale=4):
+                with gr.Row():
+                    output_model_obj = gr.Model3D(
+                        label="Output Model (OBJ Format)",
+                        camera_position=(90.0, 90.0, 3.5),
+                        interactive=False,
+                    )
+                with gr.Row():
+                    gr.Markdown('''*please note that the model is fliped due to the gradio viewer, please download the obj file and you will get the correct orientation.''')
+                    
+                with gr.Row():
+                    view_front = gr.Image(label="Front", interactive=True, show_label=True)
+                    view_right = gr.Image(label="Right", interactive=True, show_label=True)
+                    view_back = gr.Image(label="Back", interactive=True, show_label=True)
+                    view_left = gr.Image(label="Left", interactive=True, show_label=True)
+                    
+                with gr.Accordion('Advanced options', open=False):
+                    with gr.Row(equal_height=True):
+                        run_mv_btn = gr.Button('Only Generate 2D', interactive=True)
+                        run_3d_btn = gr.Button('Only Generate 3D', interactive=True)
+
+                with gr.Accordion('Advanced options (2D)', open=False):
+                    with gr.Row():
+                        foreground_ratio = gr.Slider(
+                                label="Foreground Ratio",
+                                minimum=0.5,
+                                maximum=1.0,
+                                value=1.0,
+                                step=0.05,
+                            )
+                        
+                    with gr.Row():
+                        background_choice = gr.Dropdown(label="Backgroud Choice", value="Auto Remove Background",choices=list(background_choice.keys()))
+                        rmbg_type = gr.Dropdown(label="Backgroud Remove Type", value="rembg",choices=['sam', "rembg"])
+                        backgroud_color = gr.ColorPicker(label="Background Color", value="#FFFFFF", interactive=True)
+                        # backgroud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=True)
+                        
+                    with gr.Row():
+                        mvimg_guidance_scale = gr.Number(value=4.0, minimum=3, maximum=10, label="2D Guidance Scale")
+                        mvimg_steps = gr.Number(value=30, minimum=20, maximum=100, label="2D Sample Steps")
+            
+                with gr.Accordion('Advanced options (3D)', open=False):
+                    with gr.Row():
+                        guidance_scale = gr.Number(label="3D Guidance Scale", value=7.5, minimum=3.0, maximum=10.0)
+                        steps = gr.Number(value=50, minimum=20, maximum=100, label="3D Sample Steps")
+                        
+                    with gr.Row():
+                        scheduler = gr.Dropdown(label="scheluder", value="DDIMScheduler",choices=list(scheluder_dict.keys()))
+                        octree_depth = gr.Slider(label="Octree Depth", value=7, minimum=4, maximum=8, step=1)
+                    
+        gr.Markdown(_CITE_)
+
+        outputs = [output_model_obj]
+        rmbg = RMBG(device)
+        
+        model = load_model(ckpt_path, config_path, device)
+
+        run_btn.click(fn=check_input_image, inputs=[image_input]
+                    ).success(
+                            fn=rmbg.run, 
+                            inputs=[rmbg_type, image_input, foreground_ratio, background_choice, backgroud_color],
+                            outputs=[image_input]
+                    ).success(
+                            fn=gen_mvimg,
+                            inputs=[mvimg_model, image_input, seed, mvimg_guidance_scale, mvimg_steps, text, neg_text, elevation],
+                            outputs=[view_front, view_right, view_back, view_left]
+                    ).success(
+                            fn=image2mesh, 
+                            inputs=[view_front, view_right, view_back, view_left, more, scheduler, guidance_scale, seed, octree_depth],
+                            outputs=outputs, 
+                            api_name="generate_img2obj")
+        run_mv_btn.click(fn=gen_mvimg,
+                        inputs=[mvimg_model, image_input, seed, mvimg_guidance_scale, mvimg_steps, text, neg_text, elevation],
+                        outputs=[view_front, view_right, view_back, view_left]
+        )
+        run_3d_btn.click(fn=image2mesh, 
+                        inputs=[view_front, view_right, view_back, view_left, more, scheduler, guidance_scale, seed, octree_depth],
+                        outputs=outputs, 
+                        api_name="generate_img2obj")
+        
         demo.queue().launch(share=True, allowed_paths=[args.cached_dir])