Update app.py

app.py

@@ -1,121 +1,121 @@
 import spaces
 from spaces.zero.decorator import GPU
 
-import os
-import tyro
-import imageio
 import numpy as np
 import tqdm
 import torch
+import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.transforms.functional as TF
 from safetensors.torch import load_file
 import rembg
 import gradio as gr
-
 import kiui
 from kiui.op import recenter
 from kiui.cam import orbit_camera
-from core.utils import get_rays
+from core.utils import get_rays, grid_distortion, orbit_camera_jitter
+
 from core.options import AllConfigs, Options
-from core.models import LTRFM_Mesh, LTRFM_NeRF
+from core.models import LTRFM_Mesh,LTRFM_NeRF
 from core.instant_utils.mesh_util import save_obj, save_obj_with_mtl
 from mvdream.pipeline_mvdream import MVDreamPipeline
 from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
 from huggingface_hub import hf_hub_download
 
+import spaces
 
 IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
-IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
-GRADIO_VIDEO_PATH = 'gradio_output.mp4'
-GRADIO_OBJ_PATH = 'gradio_output_rgb.obj'
 GRADIO_OBJ_ALBEDO_PATH = 'gradio_output_albedo.obj'
 GRADIO_OBJ_SHADING_PATH = 'gradio_output_shading.obj'
 
+#opt = tyro.cli(AllConfigs)
+
 ckpt_path = hf_hub_download(repo_id="rgxie/LDM", filename="LDM_6V_SDF.ckpt")
 
 opt = Options(
-    input_size=512,
+    input_size=512, 
     down_channels=(32, 64, 128, 256, 512),
     down_attention=(False, False, False, False, True),
     up_channels=(512, 256, 128),
     up_attention=(True, False, False, False),
     volume_mode='TRF_NeRF',
     splat_size=64,
-    output_size=62,  # crop patch
+    output_size=62, #crop patch
     data_mode='s5',
     num_views=8,
-    gradient_accumulation_steps=1,
+    gradient_accumulation_steps=1,  #2
     mixed_precision='bf16',
     resume=ckpt_path,
 )
 
-# Model selection
+
+# model
 if opt.volume_mode == 'TRF_Mesh':
     model = LTRFM_Mesh(opt)
 elif opt.volume_mode == 'TRF_NeRF':
     model = LTRFM_NeRF(opt)
 else:
-    model = None
+    model = LGM(opt)
 
-# Resume pretrained checkpoint
-if opt.resume:
+# resume pretrained checkpoint
+if opt.resume is not None:
     if opt.resume.endswith('safetensors'):
         ckpt = load_file(opt.resume, device='cpu')
-    else:
+    else: #ckpt
         ckpt_dict = torch.load(opt.resume, map_location='cpu')
-        ckpt = ckpt_dict["model"]
+        ckpt=ckpt_dict["model"]
 
     state_dict = model.state_dict()
     for k, v in ckpt.items():
-        k = k.replace('module.', '')
-        if k in state_dict:
+        k=k.replace('module.', '')
+        if k in state_dict: 
             if state_dict[k].shape == v.shape:
                 state_dict[k].copy_(v)
             else:
                 print(f'[WARN] mismatching shape for param {k}: ckpt {v.shape} != model {state_dict[k].shape}, ignored.')
         else:
             print(f'[WARN] unexpected param {k}: {v.shape}')
-    print('[INFO] load resume success!')
+    print(f'[INFO] load resume success!')
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 model = model.half().to(device)
-model.eval()
-
-tan_half_fov = np.tan(0.5 * np.deg2rad(opt.fovy))
-proj_matrix = torch.zeros(4, 4, dtype=torch.float32).to(device)
-proj_matrix[0, 0] = 1 / tan_half_fov
-proj_matrix[1, 1] = 1 / tan_half_fov
-proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear)
 proj_matrix[3, 2] = - (opt.zfar * opt.znear) / (opt.zfar - opt.znear)
 proj_matrix[2, 3] = 1
 
-# Load dreams
+# load dreams
 pipe_text = MVDreamPipeline.from_pretrained(
-    'ashawkey/mvdream-sd2.1-diffusers',
+    'ashawkey/mvdream-sd2.1-diffusers', # remote weights
     torch_dtype=torch.float16,
     trust_remote_code=True,
+    # local_files_only=True,
 )
 pipe_text = pipe_text.to(device)
 
+# mvdream
 pipe_image = MVDreamPipeline.from_pretrained(
-    "ashawkey/imagedream-ipmv-diffusers",
+    "ashawkey/imagedream-ipmv-diffusers", # remote weights
     torch_dtype=torch.float16,
     trust_remote_code=True,
+    # local_files_only=True,
 )
 pipe_image = pipe_image.to(device)
 
+
+print('Loading 123plus model ...')
 pipe_image_plus = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.2",
+    "sudo-ai/zero123plus-v1.2", 
     custom_pipeline="zero123plus",
     torch_dtype=torch.float16,
     trust_remote_code=True,
+    #local_files_only=True,
 )
 pipe_image_plus.scheduler = EulerAncestralDiscreteScheduler.from_config(
     pipe_image_plus.scheduler.config, timestep_spacing='trailing'
 )
 
-unet_path = './pretrained/diffusion_pytorch_model.bin'
+unet_path='./pretrained/diffusion_pytorch_model.bin' 
 
+print('Loading custom white-background unet ...')
 if os.path.exists(unet_path):
     unet_ckpt_path = unet_path
 else:
@@ -125,140 +125,329 @@ state_dict = torch.load(unet_ckpt_path, map_location='cpu')
 pipe_image_plus.unet.load_state_dict(state_dict, strict=True)
 pipe_image_plus = pipe_image_plus.to(device)
 
-# Load rembg
+# load rembg
 bg_remover = rembg.new_session()
 
+
 @spaces.GPU
 def generate_mv(condition_input_image, prompt, prompt_neg='', input_elevation=0, input_num_steps=30, input_seed=42, mv_moedl_option=None):
+    # seed
     kiui.seed_everything(input_seed)
-    os.makedirs(os.path.join(opt.workspace, "gradio"), exist_ok=True)
 
+    os.makedirs(os.path.join(opt.workspace, "gradio"), exist_ok=True)
+    
+    # text-conditioned
     if condition_input_image is None:
         mv_image_uint8 = pipe_text(prompt, negative_prompt=prompt_neg, num_inference_steps=input_num_steps, guidance_scale=7.5, elevation=input_elevation)
         mv_image_uint8 = (mv_image_uint8 * 255).astype(np.uint8)
-
+        # bg removal
         mv_image = []
         for i in range(4):
-            image = rembg.remove(mv_image_uint8[i], session=bg_remover)
+            image = rembg.remove(mv_image_uint8[i], session=bg_remover) # [H, W, 4]
+            # to white bg
             image = image.astype(np.float32) / 255
             image = recenter(image, image[..., 0] > 0, border_ratio=0.2)
             image = image[..., :3] * image[..., -1:] + (1 - image[..., -1:])
             mv_image.append(image)
-
-        mv_image_grid = np.concatenate([mv_image[1], mv_image[2], mv_image[3], mv_image[0]], axis=1)
+            
+        mv_image_grid = np.concatenate([mv_image[1], mv_image[2],mv_image[3], mv_image[0]],axis=1)
         input_image = np.stack([mv_image[1], mv_image[2], mv_image[3], mv_image[0]], axis=0)
-        processed_image = None
-
+        
+        processed_image=None
+    # image-conditioned (may also input text, but no text usually works too)
     else:
-        condition_input_image = np.array(condition_input_image)
-        carved_image = rembg.remove(condition_input_image, session=bg_remover)
+        condition_input_image = np.array(condition_input_image) # uint8
+        # bg removal
+        carved_image = rembg.remove(condition_input_image, session=bg_remover) # [H, W, 4]
         mask = carved_image[..., -1] > 0
         image = recenter(carved_image, mask, border_ratio=0.2)
         image = image.astype(np.float32) / 255.0
         processed_image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])
-
-        if mv_moedl_option == 'mvdream':
-            mv_image = pipe_image(prompt, processed_image, negative_prompt=prompt_neg, num_inference_steps=input_num_steps, guidance_scale=5.0, elevation=input_elevation)
-            mv_image_grid = np.concatenate([mv_image[1], mv_image[2], mv_image[3], mv_image[0]], axis=1)
+        
+        if mv_moedl_option=='mvdream':
+            mv_image = pipe_image(prompt, processed_image, negative_prompt=prompt_neg, num_inference_steps=input_num_steps, guidance_scale=5.0,  elevation=input_elevation)
+        
+            mv_image_grid = np.concatenate([mv_image[1], mv_image[2],mv_image[3], mv_image[0]],axis=1)
             input_image = np.stack([mv_image[1], mv_image[2], mv_image[3], mv_image[0]], axis=0)
 
         else:
             from PIL import Image
-            from einops import rearrange
-
+            from einops import rearrange, repeat
+            
+            # input_image=input_image* 255
             processed_image = Image.fromarray((processed_image * 255).astype(np.uint8))
             mv_image = pipe_image_plus(processed_image, num_inference_steps=input_num_steps).images[0]
             mv_image = np.asarray(mv_image, dtype=np.float32) / 255.0
-            mv_image = torch.from_numpy(mv_image).permute(2, 0, 1).contiguous().float()
+            mv_image = torch.from_numpy(mv_image).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
             mv_image_grid = rearrange(mv_image, 'c (n h) (m w) -> (m h) (n w) c', n=3, m=2).numpy()
             mv_image = rearrange(mv_image, 'c (n h) (m w) -> (n m) h w c', n=3, m=2).numpy()
             input_image = mv_image
+    return mv_image_grid, processed_image, input_image 
 
-    return mv_image_grid, processed_image, input_image
 
 @spaces.GPU
 def generate_3d(input_image, condition_input_image, mv_moedl_option=None, input_seed=42):
     kiui.seed_everything(input_seed)
+    
+    output_obj_rgb_path = os.path.join(opt.workspace,"gradio", GRADIO_OBJ_PATH)
+    output_obj_albedo_path = os.path.join(opt.workspace,"gradio", GRADIO_OBJ_ALBEDO_PATH)
+    output_obj_shading_path = os.path.join(opt.workspace,"gradio", GRADIO_OBJ_SHADING_PATH)
+    
+    output_video_path = os.path.join(opt.workspace,"gradio", GRADIO_VIDEO_PATH)
+    # generate gaussians
+     # [4, 256, 256, 3], float32
+    input_image = torch.from_numpy(input_image).permute(0, 3, 1, 2).float().to(device) # [4, 3, 256, 256]
+    input_image = F.interpolate(input_image, size=(opt.input_size, opt.input_size), mode='bilinear', align_corners=False)
 
-    output_obj_rgb_path = os.path.join(opt.workspace, "gradio", GRADIO_OBJ_PATH)
-    output_obj_albedo_path = os.path.join(opt.workspace, "gradio", GRADIO_OBJ_ALBEDO_PATH)
-    output_obj_shading_path = os.path.join(opt.workspace, "gradio", GRADIO_OBJ_SHADING_PATH)
-    output_video_path = os.path.join(opt.workspace, "gradio", GRADIO_VIDEO_PATH)
+    images_input_vit = F.interpolate(input_image, size=(224, 224), mode='bilinear', align_corners=False)
+    
+    data = {}
+    input_image = input_image.unsqueeze(0) # [1, 4, 9, H, W]
+    images_input_vit=images_input_vit.unsqueeze(0)
+    data['input_vit']=images_input_vit
+    
+    elevation = 0
+    cam_poses =[]
+    if mv_moedl_option=='mvdream' or condition_input_image is None:
+            azimuth = np.arange(0, 360, 90, dtype=np.int32)
+            for azi in tqdm.tqdm(azimuth):
+                cam_pose = torch.from_numpy(orbit_camera(elevation, azi, radius=opt.cam_radius, opengl=True)).unsqueeze(0).to(device)
+                cam_poses.append(cam_pose)
+    else:
+        azimuth = np.arange(30, 360, 60, dtype=np.int32)
+        cnt = 0
+        for azi in tqdm.tqdm(azimuth):
+            if (cnt+1) % 2!= 0:
+                elevation=-20
+            else:
+                elevation=30
+            cam_pose = torch.from_numpy(orbit_camera(elevation, azi, radius=opt.cam_radius, opengl=True)).unsqueeze(0).to(device)
+            cam_poses.append(cam_pose)
+            cnt=cnt+1
+            
+    cam_poses = torch.cat(cam_poses,0)
+    radius = torch.norm(cam_poses[0, :3, 3])
+    cam_poses[:, :3, 3] *= opt.cam_radius / radius
+    transform = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, opt.cam_radius], [0, 0, 0, 1]], dtype=torch.float32).to(device) @ torch.inverse(cam_poses[0])
+    cam_poses = transform.unsqueeze(0) @ cam_poses 
+    
+    cam_poses=cam_poses.unsqueeze(0)
+    data['source_camera']=cam_poses
+    
+    with torch.no_grad():
+        if opt.volume_mode == 'TRF_Mesh':
+            with torch.autocast(device_type='cuda', dtype=torch.float32):
+                svd_volume = model.forward_svd_volume(input_image,data)
+        else:
+            with torch.autocast(device_type='cuda', dtype=torch.float16):
+                svd_volume = model.forward_svd_volume(input_image,data)
+        
+        #time-consuming
+        export_texmap=False
+        
+        mesh_out = model.extract_mesh(svd_volume,use_texture_map=export_texmap)
+        
+        if export_texmap:
+            vertices, faces, uvs, mesh_tex_idx, tex_map = mesh_out
+            
+            for i in range(len(tex_map)):
+                mesh_path=os.path.join(opt.workspace, name + str(i) + '_'+ str(seed)+ '.obj')
+                save_obj_with_mtl(
+                    vertices.data.cpu().numpy(),
+                    uvs.data.cpu().numpy(),
+                    faces.data.cpu().numpy(),
+                    mesh_tex_idx.data.cpu().numpy(),
+                    tex_map[i].permute(1, 2, 0).data.cpu().numpy(),
+                    mesh_path,
+                )
+        else:
+            vertices, faces, vertex_colors = mesh_out
 
-    input_image = torch.from_numpy(input_image).permute(0, 3, 1, 2).float().to(device)
-    input_image = F.interpolate(input_image, size=(opt.input_size, opt.input_size), mode='bilinear', align_corners=False)
+            save_obj(vertices, faces, vertex_colors[0], output_obj_rgb_path)
+            save_obj(vertices, faces, vertex_colors[1], output_obj_albedo_path)
+            save_obj(vertices, faces, vertex_colors[2], output_obj_shading_path)
+        
+        # images=[]  
+        # azimuth = np.arange(0, 360, 6, dtype=np.int32)
+        # for azi in tqdm.tqdm(azimuth):
+
+        #     cam_pose = torch.from_numpy(orbit_camera(elevation, azi, radius=opt.cam_radius, opengl=True))
+
+        #     if opt.volume_mode == 'TRF_Mesh': 
+        #         cam_view = torch.inverse(cam_pose)
+        #         cam_view=cam_view.unsqueeze(0).unsqueeze(0).to(device)
+        #         data['w2c'] = cam_view
+        #         with torch.autocast(device_type='cuda', dtype=torch.float32):
+        #             render_images=model.render_frame(data)
+        #     else:
+        #         rays_o, rays_d = get_rays(cam_pose, opt.infer_render_size, opt.infer_render_size, opt.fovy) # [h, w, 3]
+        #         rays_o=rays_o.unsqueeze(0).unsqueeze(0).to(device)# B,V,H,W,3
+        #         rays_d=rays_d.unsqueeze(0).unsqueeze(0).to(device)
+        #         data['all_rays_o']=rays_o
+        #         data['all_rays_d']=rays_d
+        #         with torch.autocast(device_type='cuda', dtype=torch.float16):
+        #             render_images=model.render_frame(data)
+        #     image=render_images['images_pred']
+
+        #     images.append((image.squeeze(1).permute(0,2,3,1).contiguous().float().cpu().numpy() * 255).astype(np.uint8))
+
+        # images = np.concatenate(images, axis=0)
+        # imageio.mimwrite(output_video_path, images, fps=30)
+        
+
+    return output_obj_rgb_path, output_obj_albedo_path, output_obj_shading_path #, output_video_path
+
+
+# gradio UI
+
+_TITLE = '''LDM: Large Tensorial SDF Model for Textured Mesh Generation'''
+
+_DESCRIPTION = '''
+
+
+
+* Input can be text prompt, image. 
+* The currently supported multi-view diffusion models include the image-conditioned MVdream and Zero123plus, as well as the text-conditioned Imagedream.
+* If you find the output unsatisfying, try using different multi-view diffusion models or seeds!
+'''
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+block = gr.Blocks(title=_TITLE).queue()
+with block:
 
-    input_rays_o, input_rays_d = get_rays(opt, proj_matrix, device, 'center')
 
-    with torch.no_grad():
-        preds = model(
-            cond_img=input_image,
-            rays=(input_rays_o, input_rays_d)
-        )
-
-    pred_rgb = preds[0].permute(0, 2, 3, 1).contiguous().cpu().numpy()
-    pred_albedo = preds[1].permute(0, 2, 3, 1).contiguous().cpu().numpy()
-    pred_shading = preds[2].permute(0, 2, 3, 1).contiguous().cpu().numpy()
-
-    save_obj(output_obj_rgb_path, pred_rgb)
-    save_obj_with_mtl(output_obj_albedo_path, pred_albedo, mode="albedo")
-    save_obj_with_mtl(output_obj_shading_path, pred_shading, mode="shading")
-
-    camera_positions = orbit_camera(type="spherical", radius=2.5, h=3, w=2)
-    output_frames = []
-    for pose in tqdm.tqdm(camera_positions, ncols=0):
-        with torch.no_grad():
-            preds = model(cond_img=input_image, rays=get_rays(opt, proj_matrix, device, pose))
-        pred_rgb = preds[0].permute(0, 2, 3, 1).contiguous().cpu().numpy()
-        output_frames.append(pred_rgb)
-    output_frames = np.stack(output_frames, axis=0)
-
-    imageio.mimwrite(output_video_path, output_frames, fps=24, quality=8)
-    return output_obj_rgb_path, output_obj_albedo_path, output_obj_shading_path, output_video_path
-
-def update_mv_model(mv_moedl_option):
-    if mv_moedl_option == 'mvdream':
-        return gr.update(visible=False)
-    else:
-        return gr.update(visible=True)
 
-# Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown(
-        "## Generate 3D object from text or image prompt"
-    )
     with gr.Row():
-        with gr.Column():
-            input_prompt = gr.Textbox(label="Prompt", lines=3)
-            input_image = gr.Image(label="Input Image", type='numpy', optional=True)
-            input_seed = gr.Slider(minimum=0, maximum=65535, step=1, label="Random Seed", value=42)
-            input_elevation = gr.Slider(minimum=-10, maximum=10, step=1, label="Elevation", value=0)
-            input_num_steps = gr.Slider(minimum=1, maximum=150, step=1, label="Number of Inference Steps", value=30)
-            mv_moedl_option = gr.Radio(
-                ["mvdream", "zero123plus"],
-                label="Model Option",
-                value="mvdream",
-                interactive=True
-            )
-            generate_mv_button = gr.Button(value="Generate Multi-View Images")
-            generate_mv_button.click(fn=generate_mv,
-                                    inputs=[input_image, input_prompt, '', input_elevation, input_num_steps, input_seed, mv_moedl_option],
-                                    outputs=['multi_view_output', 'processed_image_output', 'input_image_output'])
-
-        with gr.Column():
-            gr.Markdown("### Multi-View Images")
-            multi_view_output = gr.Image()
-            processed_image_output = gr.Image()
-            gr.Markdown("### Input Image (Processed)")
-            input_image_output = gr.Image()
-            generate_3d_button = gr.Button(value="Generate 3D Model")
-            generate_3d_button.click(fn=generate_3d,
-                                    inputs=[input_image_output, processed_image_output, mv_moedl_option, input_seed],
-                                    outputs=['output_obj_rgb', 'output_obj_albedo', 'output_obj_shading', 'output_video'])
-
-    output_obj_rgb = gr.File(label="RGB 3D Model (.obj)")
-    output_obj_albedo = gr.File(label="Albedo 3D Model (.obj)")
-    output_obj_shading = gr.File(label="Shading 3D Model (.obj)")
-    output_video = gr.Video(label="360° View of the Generated 3D Model (.mp4)")
-
-demo.launch()
+        with gr.Column(scale=1):
+            gr.Markdown('# ' + _TITLE)
+    gr.Markdown(_DESCRIPTION)
+    
+    with gr.Row(variant='panel'):
+        with gr.Column(scale=1):
+            with gr.Tab("Image-to-3D"):
+                # input image
+                with gr.Row():
+                    condition_input_image = gr.Image(
+                        label="Input Image", 
+                        image_mode="RGBA", 
+                        type="pil"
+                    )
+                    
+                    processed_image = gr.Image(
+                        label="Processed Image", 
+                        image_mode="RGBA", 
+                        type="pil", 
+                        interactive=False
+                    )
+                
+                
+                with gr.Row():
+                        mv_moedl_option = gr.Radio([
+                                "zero123plus",
+                                "mvdream"
+                            ], value="zero123plus",
+                            label="Multi-view Diffusion")
+                        
+                with gr.Row(variant="panel"):
+                    gr.Examples(
+                        examples=[
+                            os.path.join("example", img_name) for img_name in sorted(os.listdir("example"))
+                        ],
+                        inputs=[condition_input_image],
+                        fn=lambda x: process(condition_input_image=x, prompt=''),
+                        cache_examples=False,
+                        examples_per_page=20,
+                        label='Image-to-3D Examples'
+                    )
+                
+            with gr.Tab("Text-to-3D"):  
+                # input prompt
+                with gr.Row():
+                    input_text = gr.Textbox(label="prompt")
+                # negative prompt
+                with gr.Row():
+                    input_neg_text = gr.Textbox(label="negative prompt", value='ugly, blurry, pixelated obscure, unnatural colors, poor lighting, dull, unclear, cropped, lowres, low quality, artifacts, duplicate')
+
+                with gr.Row(variant="panel"):
+                    gr.Examples(
+                        examples=[
+                            "a hamburger",
+                            "a furry red fox head",
+                            "a teddy bear",
+                            "a motorbike",
+                        ],
+                        inputs=[input_text],
+                        fn=lambda x: process(condition_input_image=None, prompt=x),
+                        cache_examples=False,
+                        label='Text-to-3D Examples'
+                    )
+            
+            # elevation
+            input_elevation = gr.Slider(label="elevation", minimum=-90, maximum=90, step=1, value=0)
+            # inference steps
+            input_num_steps = gr.Slider(label="inference steps", minimum=1, maximum=100, step=1, value=30)
+            # random seed
+            input_seed = gr.Slider(label="random seed", minimum=0, maximum=100000, step=1, value=0)
+            # gen button
+            button_gen = gr.Button("Generate")
+
+        
+        with gr.Column(scale=1):
+            with gr.Row():
+                # multi-view results
+                mv_image_grid = gr.Image(interactive=False, show_label=False)
+            # with gr.Row():    
+            #     output_video_path = gr.Video(label="video")
+            with gr.Row():    
+                output_obj_rgb_path = gr.Model3D(
+                    label="RGB Model (OBJ Format)",
+                    interactive=False,
+                )
+            with gr.Row():    
+                output_obj_albedo_path = gr.Model3D(
+                    label="Albedo Model (OBJ Format)",
+                    interactive=False,
+                )
+            with gr.Row():
+                output_obj_shading_path = gr.Model3D(
+                    label="Shading Model (OBJ Format)",
+                    interactive=False,
+                )
+
+            
+        input_image = gr.State()
+        button_gen.click(fn=generate_mv, inputs=[condition_input_image, input_text, input_neg_text, input_elevation, input_num_steps, input_seed, mv_moedl_option], 
+                         outputs=[mv_image_grid, processed_image, input_image],).success(
+                            fn=generate_3d,
+                            inputs=[input_image, condition_input_image, mv_moedl_option, input_seed], 
+                            outputs=[output_obj_rgb_path, output_obj_albedo_path, output_obj_shading_path] , #output_video_path
+                         )
+        
+        
+block.launch(server_name="0.0.0.0", share=False)