initial code

.gitattributes

@@ -32,4 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,147 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug
+.idea/
+cloud_tools/
+
+output
+pretrained_models
+results
+develop
+gradio_results
+demo

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "extern/LGM"]
+	path = extern/LGM
+	url = https://github.com/3DTopia/LGM.git

LICENSE

@@ -0,0 +1,13 @@
+S-Lab License 1.0
+Copyright 2024 S-Lab
+
+Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work.

README.md

@@ -1,10 +1,10 @@
 ---
 title: 3DEnhancer
-emoji: 📚
-colorFrom: green
-colorTo: pink
+emoji: 🔆
+colorFrom: red
+colorTo: green
 sdk: gradio
-sdk_version: 5.20.1
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: other

app.py

@@ -0,0 +1,588 @@
+import warnings
+warnings.filterwarnings('ignore')
+
+import spaces
+
+import os
+import tyro
+import imageio
+import numpy as np
+import tqdm
+import cv2
+import torch
+import torch.nn.functional as F
+from torchvision import transforms as T
+import torchvision.transforms.functional as TF
+from safetensors.torch import load_file
+import kiui
+from kiui.op import recenter
+from kiui.cam import orbit_camera
+import rembg
+import gradio as gr
+from gradio_imageslider import ImageSlider
+
+import sys
+sys.path.insert(0, "src")
+from src.enhancer import Enhancer
+from src.utils.camera import get_c2ws
+
+# import LGM
+sys.path.insert(0, "extern/LGM")
+from core.options import AllConfigs
+from core.models import LGM
+from mvdream.pipeline_mvdream import MVDreamPipeline
+
+
+# download checkpoints
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="ashawkey/LGM", filename="model_fp16_fixrot.safetensors", local_dir='pretrained_models/LGM')
+hf_hub_download(repo_id="Luo-Yihang/3DEnhancer", filename="model.safetensors", local_dir='pretrained_models/3DEnhancer')
+
+
+### Title and Description ###
+#### Description ####
+title = r"""<h1 align="center">3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement</h1>"""
+
+important_link = r"""
+<div align='center'>
+<a href='https://arxiv.org/abs/2412.18565'>[arxiv]</a>
+&ensp; <a href='https://Luo-Yihang.github.io/projects/3DEnhancer'>[Project Page]</a>
+&ensp; <a href='https://github.com/Luo-Yihang/3DEnhancer'>[Code]</a>
+</div>
+"""
+
+authors = r"""
+<div align='center'>
+ <a href='https://github.com/Luo-Yihang'>Yihang Luo</a>
+ &ensp; <a href='https://shangchenzhou.com/'>Shangchen Zhou</a>
+&ensp; <a href='https://nirvanalan.github.io/'>Yushi Lan</a>
+&ensp; <a href='https://xingangpan.github.io/'>Xingang Pan</a>
+&ensp; <a href='https://www.mmlab-ntu.com/person/ccloy/index.html'>Chen Change Loy</a>
+</div>
+"""
+
+affiliation = r"""
+<div align='center'>
+ <a href='https://www.mmlab-ntu.com/'>S-Lab, NTU Singapore</a>
+</div>
+"""
+
+description = r"""
+<b>Official Gradio demo</b> for <a href='https://yihangluo.com/projects/3DEnhancer' target='_blank'><b>3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement</b></a>.<br>
+🔥 3DEnhancer employs a multi-view diffusion model to enhance multi-view images, thus improving 3D models. Our contributions include a robust data augmentation pipeline, and the view-consistent blocks that integrate multi-view row attention and near-view epipolar aggregation modules to promote view consistency. <br>
+"""
+
+article = r"""
+<br>If 3DEnhancer is helpful, please help to ⭐ the <a href='https://github.com/Luo-Yihang/3DEnhancer' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/Luo-Yihang/3DEnhancer)](https://github.com/Luo-Yihang/3DEnhancer)
+---
+📝 **License**
+<br>
+This project is licensed under <a href="https://github.com/Luo-Yihang/3DEnhancer/blob/main/LICENSE">S-Lab License 1.0</a>, 
+Redistribution and use for non-commercial purposes should follow this license.
+<br>
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+```bibtex
+@article{luo20243denhancer,
+    title={3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement}, 
+    author={Yihang Luo and Shangchen Zhou and Yushi Lan and Xingang Pan and Chen Change Loy},
+    booktitle={arXiv preprint arXiv:2412.18565}
+    year={2024},
+}
+```
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>[email protected]</b>.
+"""
+
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+BASE_SAVE_PATH = 'gradio_results'
+GRADIO_VIDEO_PATH = f'{BASE_SAVE_PATH}/gradio_output.mp4'
+GRADIO_PLY_PATH = f'{BASE_SAVE_PATH}/gradio_output.ply'
+GRADIO_ENHANCED_VIDEO_PATH = f'{BASE_SAVE_PATH}/gradio_enhanced_output.mp4'
+GRADIO_ENHANCED_PLY_PATH = f'{BASE_SAVE_PATH}/gradio_enhanced_output.ply'
+DEFAULT_NEG_PROMPT = "ugly, blurry, pixelated obscure, unnatural colors, poor lighting, dull, unclear, cropped, lowres, low quality, artifacts, duplicate"
+DEFAULT_SEED = 0
+os.makedirs(BASE_SAVE_PATH, exist_ok=True)
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# load dreams
+pipe_text = MVDreamPipeline.from_pretrained(
+    'ashawkey/mvdream-sd2.1-diffusers', # remote weights
+    torch_dtype=torch.float16,
+	trust_remote_code=True
+)
+pipe_text = pipe_text.to(device)
+
+pipe_image = MVDreamPipeline.from_pretrained(
+    "ashawkey/imagedream-ipmv-diffusers", # remote weights
+    torch_dtype=torch.float16,
+	trust_remote_code=True
+)
+pipe_image = pipe_image.to(device)
+
+# load lgm
+lgm_opt = tyro.cli(AllConfigs, args=["big"])
+
+tan_half_fov = np.tan(0.5 * np.deg2rad(lgm_opt.fovy))
+proj_matrix = torch.zeros(4, 4, dtype=torch.float32, device=device)
+proj_matrix[0, 0] = 1 / tan_half_fov
+proj_matrix[1, 1] = 1 / tan_half_fov
+proj_matrix[2, 2] = (lgm_opt.zfar + lgm_opt.znear) / (lgm_opt.zfar - lgm_opt.znear)
+proj_matrix[3, 2] = - (lgm_opt.zfar * lgm_opt.znear) / (lgm_opt.zfar - lgm_opt.znear)
+proj_matrix[2, 3] = 1
+
+lgm_model = LGM(lgm_opt)
+lgm_model = lgm_model.half().to(device)
+ckpt = load_file("pretrained_models/LGM/model_fp16_fixrot.safetensors", device='cpu')
+lgm_model.load_state_dict(ckpt, strict=False)
+lgm_model.eval()
+
+# load 3denhancer
+enhancer = Enhancer(
+	model_path = "pretrained_models/3DEnhancer/model.safetensors",
+	config_path = "src/configs/config.py",
+)
+
+# load rembg
+bg_remover = rembg.new_session()
+
+@torch.no_grad()
+@spaces.GPU
+def gen_mv(ref_image, ref_text):
+	kiui.seed_everything(DEFAULT_SEED)
+
+	# text-conditioned
+	if ref_image is None:
+		mv_image_uint8 = pipe_text(ref_text, negative_prompt=DEFAULT_NEG_PROMPT, num_inference_steps=30, guidance_scale=7.5, elevation=0)
+		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) # [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)
+	# image-conditioned (may also input text, but no text usually works too)
+	else:
+		ref_image = np.array(ref_image) # uint8
+		# bg removal
+		carved_image = rembg.remove(ref_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
+		image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])
+		mv_image = pipe_image(ref_text, image, negative_prompt=DEFAULT_NEG_PROMPT, num_inference_steps=30, guidance_scale=5.0, elevation=0)
+
+	# mv_image, a list of 4 np_arrays in shape (256, 256, 3) in range (0.0, 1.0)
+	mv_image_512 = []
+	for i in range(len(mv_image)):
+		mv_image_512.append(cv2.resize(mv_image[i], (512, 512), interpolation=cv2.INTER_LINEAR))
+
+	return mv_image_512[0], mv_image_512[1], mv_image_512[2], mv_image_512[3], ref_text, 120
+
+
+@torch.no_grad()
+@spaces.GPU
+def gen_3d(image_0, image_1, image_2, image_3, elevation, output_video_path, output_ply_path):
+	kiui.seed_everything(DEFAULT_SEED)
+
+	mv_image = [image_0, image_1, image_2, image_3]
+	for i in range(len(mv_image)):
+		if type(mv_image[i]) is tuple:
+			mv_image[i] = mv_image[i][1]
+		mv_image[i] = np.array(mv_image[i]).astype(np.float32) / 255.0
+		mv_image[i] = cv2.resize(mv_image[i], (256, 256), interpolation=cv2.INTER_AREA)
+
+	# generate gaussians
+	input_image = np.stack([mv_image[1], mv_image[2], mv_image[3], mv_image[0]], axis=0) # [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=(lgm_opt.input_size, lgm_opt.input_size), mode='bilinear', align_corners=False)
+	input_image = TF.normalize(input_image, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
+
+	rays_embeddings = lgm_model.prepare_default_rays(device, elevation=elevation)
+	input_image = torch.cat([input_image, rays_embeddings], dim=1).unsqueeze(0) # [1, 4, 9, H, W]
+
+	with torch.no_grad():
+		with torch.autocast(device_type='cuda', dtype=torch.float16):
+				# generate gaussians
+				gaussians = lgm_model.forward_gaussians(input_image)
+		lgm_model.gs.save_ply(gaussians, output_ply_path)
+		
+		# render 360 video 
+		images = []
+		elevation = 0
+		if lgm_opt.fancy_video:
+			azimuth = np.arange(0, 720, 4, dtype=np.int32)
+			for azi in tqdm.tqdm(azimuth):
+				cam_poses = torch.from_numpy(orbit_camera(elevation, azi, radius=lgm_opt.cam_radius, opengl=True)).unsqueeze(0).to(device)
+				cam_poses[:, :3, 1:3] *= -1 # invert up & forward direction
+				
+				# cameras needed by gaussian rasterizer
+				cam_view = torch.inverse(cam_poses).transpose(1, 2) # [V, 4, 4]
+				cam_view_proj = cam_view @ proj_matrix # [V, 4, 4]
+				cam_pos = - cam_poses[:, :3, 3] # [V, 3]
+
+				scale = min(azi / 360, 1)
+
+				image = lgm_model.gs.render(gaussians, cam_view.unsqueeze(0), cam_view_proj.unsqueeze(0), cam_pos.unsqueeze(0), scale_modifier=scale)['image']
+				images.append((image.squeeze(1).permute(0,2,3,1).contiguous().float().cpu().numpy() * 255).astype(np.uint8))
+		else:
+			azimuth = np.arange(0, 360, 2, dtype=np.int32)
+			for azi in tqdm.tqdm(azimuth):
+				cam_poses = torch.from_numpy(orbit_camera(elevation, azi, radius=lgm_opt.cam_radius, opengl=True)).unsqueeze(0).to(device)
+				cam_poses[:, :3, 1:3] *= -1 # invert up & forward direction
+				
+				# cameras needed by gaussian rasterizer
+				cam_view = torch.inverse(cam_poses).transpose(1, 2) # [V, 4, 4]
+				cam_view_proj = cam_view @ proj_matrix # [V, 4, 4]
+				cam_pos = - cam_poses[:, :3, 3] # [V, 3]
+
+				image = lgm_model.gs.render(gaussians, cam_view.unsqueeze(0), cam_view_proj.unsqueeze(0), cam_pos.unsqueeze(0), scale_modifier=1)['image']
+				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_video_path, output_ply_path
+
+
+@torch.no_grad()
+@spaces.GPU
+def enhance(image_0, image_1, image_2, image_3, prompt, elevation, noise_level, cfg_scale, steps, seed, color_shift):
+	kiui.seed_everything(seed)
+
+	mv_image = [image_0, image_1, image_2, image_3]
+	img_tensor_list = []
+	for image in mv_image:
+		img_tensor_list.append(T.ToTensor()(image))
+
+	img_tensors = torch.stack(img_tensor_list)
+
+	color_shift = None if color_shift=="disabled" else color_shift
+	output_img_tensors = enhancer.inference(
+		mv_imgs=img_tensors, 
+		c2ws=get_c2ws(elevations=[elevation]*4, amuziths=[0,90,180,270]), 
+		prompt=prompt, 
+		noise_level=noise_level,
+		cfg_scale=cfg_scale,
+		sample_steps=steps,
+		color_shift=color_shift,
+	)
+
+	mv_image_512 = output_img_tensors.permute(0,2,3,1).cpu().numpy()
+
+	# return to the image slider component
+	return (image_0, mv_image_512[0]), (image_1, mv_image_512[1]), (image_2, mv_image_512[2]), (image_3, mv_image_512[3])
+
+
+def check_video(input_video):
+    if input_video:
+        return gr.update(interactive=True)
+    return gr.update(interactive=False)
+
+
+i2mv_examples = [
+	["assets/examples/i2mv/cake.png", "cake"], 
+	["assets/examples/i2mv/skull.png", "skull"],
+	["assets/examples/i2mv/sea_turtle.png", "sea turtle"],
+	["assets/examples/i2mv/house2.png", "house"],
+	["assets/examples/i2mv/cup.png", "cup"],
+	["assets/examples/i2mv/mannequin.png", "mannequin"],
+	["assets/examples/i2mv/boy.jpg", "boy"],
+	["assets/examples/i2mv/dragontoy.jpg", "dragon toy"],
+	["assets/examples/i2mv/gso_rabbit.jpg", "rabbit car"],
+	["assets/examples/i2mv/Mario_New_Super_Mario_Bros_U_Deluxe.png", "standing Mario"],
+]
+
+t2mv_examples = [
+	"teddy bear",
+	"hamburger",
+	"oldman's head sculpture",
+	"headphone",
+	"mech suit",
+	"wooden barrel",
+	"scary zombie"
+]
+
+mv_examples = [
+	[
+		"assets/examples/mv_lq_prerendered/vase.mp4",
+		"assets/examples/mv_lq/vase/00.png",
+		"assets/examples/mv_lq/vase/01.png",
+		"assets/examples/mv_lq/vase/02.png",
+		"assets/examples/mv_lq/vase/03.png",
+		"vase",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/tower.mp4",
+		"assets/examples/mv_lq/tower/00.png",
+		"assets/examples/mv_lq/tower/01.png",
+		"assets/examples/mv_lq/tower/02.png",
+		"assets/examples/mv_lq/tower/03.png",
+		"brick tower",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/truck.mp4",
+		"assets/examples/mv_lq/truck/00.png", 
+		"assets/examples/mv_lq/truck/01.png", 
+		"assets/examples/mv_lq/truck/02.png",
+		"assets/examples/mv_lq/truck/03.png",
+		"truck",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/gascan.mp4",
+		"assets/examples/mv_lq/gascan/00.png",
+		"assets/examples/mv_lq/gascan/01.png",
+		"assets/examples/mv_lq/gascan/02.png",
+		"assets/examples/mv_lq/gascan/03.png",
+		"gas can",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/fish.mp4",
+		"assets/examples/mv_lq/fish/00.png",
+		"assets/examples/mv_lq/fish/01.png",
+		"assets/examples/mv_lq/fish/02.png",
+		"assets/examples/mv_lq/fish/03.png", 
+		"sea fish with eyes",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/tshirt.mp4",
+		"assets/examples/mv_lq/tshirt/00.png",
+		"assets/examples/mv_lq/tshirt/01.png",
+		"assets/examples/mv_lq/tshirt/02.png",
+		"assets/examples/mv_lq/tshirt/03.png",
+		"t-shirt",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/turtle.mp4",
+		"assets/examples/mv_lq/turtle/00.png",
+		"assets/examples/mv_lq/turtle/01.png",
+		"assets/examples/mv_lq/turtle/02.png",
+		"assets/examples/mv_lq/turtle/03.png",
+		"sea turtle",
+		200
+	],
+	[
+		"assets/examples/mv_lq_prerendered/cake.mp4",
+		"assets/examples/mv_lq/cake/00.png",
+		"assets/examples/mv_lq/cake/01.png",
+		"assets/examples/mv_lq/cake/02.png",
+		"assets/examples/mv_lq/cake/03.png",
+		"cake",
+		120
+	],
+	[
+		"assets/examples/mv_lq_prerendered/lamp.mp4",
+		"assets/examples/mv_lq/lamp/00.png",
+		"assets/examples/mv_lq/lamp/01.png",
+		"assets/examples/mv_lq/lamp/02.png",
+		"assets/examples/mv_lq/lamp/03.png",
+		"lamp",
+		0
+	],
+	[
+		"assets/examples/mv_lq_prerendered/oldman.mp4",
+		"assets/examples/mv_lq/oldman/00.png",
+		"assets/examples/mv_lq/oldman/00.png",
+		"assets/examples/mv_lq/oldman/00.png",
+		"assets/examples/mv_lq/oldman/00.png",
+		"old man sculpture",
+		120
+	],
+	[
+		"assets/examples/mv_lq_prerendered/mario.mp4",
+		"assets/examples/mv_lq/mario/00.png",
+		"assets/examples/mv_lq/mario/01.png",
+		"assets/examples/mv_lq/mario/02.png",
+		"assets/examples/mv_lq/mario/03.png",
+		"standing mario",
+		120
+	],
+	[
+		"assets/examples/mv_lq_prerendered/house.mp4",
+		"assets/examples/mv_lq/house/00.png",
+		"assets/examples/mv_lq/house/01.png",
+		"assets/examples/mv_lq/house/02.png",
+		"assets/examples/mv_lq/house/03.png",
+		"house",
+		120
+	],
+]
+
+
+# gradio UI
+demo = gr.Blocks().queue()
+with demo:
+	gr.Markdown(title)
+	gr.Markdown(authors)
+	gr.Markdown(affiliation)
+	gr.Markdown(important_link)
+	gr.Markdown(description)
+	
+	original_video_path = gr.State(GRADIO_VIDEO_PATH)
+	original_ply_path = gr.State(GRADIO_PLY_PATH)
+	enhanced_video_path = gr.State(GRADIO_ENHANCED_VIDEO_PATH)
+	enhanced_ply_path = gr.State(GRADIO_ENHANCED_PLY_PATH)
+
+	with gr.Column(variant='panel'):
+		with gr.Accordion("Generate Multi Views (LGM)", open=False):
+			gr.Markdown("*Don't have multi-view images on hand? Generate them here using a single image, text, or a combination of both.*")
+			with gr.Row():
+				with gr.Column():
+					ref_image = gr.Image(label="Reference Image", type='pil', height=400, interactive=True)
+					ref_text = gr.Textbox(label="Prompt", value="", interactive=True)
+				with gr.Column():
+					gr.Examples(
+						examples=i2mv_examples,
+						inputs=[ref_image, ref_text],
+						examples_per_page=3,
+						label='Image-to-Multiviews Examples',
+					)
+
+					gr.Examples(
+						examples=t2mv_examples,
+						inputs=[ref_text], 
+						outputs=[ref_image, ref_text],
+						cache_examples=False,
+						run_on_click=True,
+						fn=lambda x: (None, x),
+						label='Text-to-Multiviews Examples',
+					)
+					
+			with gr.Row():
+				gr.Column()  # Empty column for spacing
+				button_gen_mv = gr.Button("Generate Multi Views", scale=1)
+				gr.Column()  # Empty column for spacing
+
+		with gr.Column():
+			gr.Markdown("Let's enhance!")
+			with gr.Row():
+				with gr.Column(scale=2):
+					with gr.Tab("Multi Views"):
+						gr.Markdown("*Upload your multi-view images and enhance them with 3DEnhancer. You can also generate 3D model using LGM.*")
+						with gr.Row():
+							input_image_0 = gr.Image(label="[Input] view-0", type='pil', height=320)
+							input_image_1 = gr.Image(label="[Input] view-1", type='pil', height=320)
+							input_image_2 = gr.Image(label="[Input] view-2", type='pil', height=320)
+							input_image_3 = gr.Image(label="[Input] view-3", type='pil', height=320)
+						gr.Markdown("---")
+						gr.Markdown("Enhanced Output")
+						with gr.Row():
+							enhanced_image_0 = ImageSlider(label="[Enhanced] view-0", type='pil', height=350, interactive=False)
+							enhanced_image_1 = ImageSlider(label="[Enhanced] view-1", type='pil', height=350, interactive=False)
+							enhanced_image_2 = ImageSlider(label="[Enhanced] view-2", type='pil', height=350, interactive=False)
+							enhanced_image_3 = ImageSlider(label="[Enhanced] view-3", type='pil', height=350, interactive=False)
+					with gr.Tab("Generated 3D"):
+						gr.Markdown("Coarse Input")
+						with gr.Column():
+							with gr.Row():
+								gr.Column()  # Empty column for spacing
+								with gr.Column():
+									input_3d_video = gr.Video(label="[Input] Rendered Video", height=300, scale=1, interactive=False)
+									with gr.Row():
+										button_gen_3d = gr.Button("Render 3D")
+										button_download_3d = gr.DownloadButton("Download Ply", interactive=False)
+										# button_download_3d = gr.File(label="Download Ply", interactive=False, height=50)
+								gr.Column()  # Empty column for spacing
+							gr.Markdown("---")
+							gr.Markdown("Enhanced Output")
+							with gr.Row():
+								gr.Column()  # Empty column for spacing
+								with gr.Column():
+									enhanced_3d_video = gr.Video(label="[Enhanced] Rendered Video", height=300, scale=1, interactive=False)
+									with gr.Row():
+										enhanced_button_gen_3d = gr.Button("Render 3D")
+										enhanced_button_download_3d = gr.DownloadButton("Download Ply", interactive=False)
+								gr.Column()  # Empty column for spacing
+			
+			with gr.Column():
+				with gr.Row():
+					enhancer_text = gr.Textbox(label="Prompt", value="", scale=1)
+					enhancer_noise_level = gr.Slider(label="enhancer noise level", minimum=0, maximum=300, step=1, value=0, interactive=True)
+				with gr.Accordion("Addvanced Setting", open=False):
+					with gr.Column():
+						with gr.Row():
+							with gr.Column():
+								elevation = gr.Slider(label="elevation", minimum=-90, maximum=90, step=1, value=0)
+								cfg_scale = gr.Slider(label="cfg scale", minimum=0, maximum=10, step=0.1, value=4.5)
+							with gr.Column():
+								seed = gr.Slider(label="random seed", minimum=0, maximum=100000, step=1, value=0)
+								steps = gr.Slider(label="inference steps", minimum=1, maximum=100, step=1, value=20)
+						with gr.Row():
+							color_shift = gr.Radio(label="color shift", value="disabled", choices=["disabled", "adain", "wavelet"])
+				with gr.Row():
+					gr.Column()  # Empty column for spacing
+					button_enhance = gr.Button("Enhance", scale=1, variant="primary")
+					gr.Column()  # Empty column for spacing
+
+			gr.Examples(
+				examples=mv_examples,
+				inputs=[input_3d_video, input_image_0, input_image_1, input_image_2, input_image_3, enhancer_text, enhancer_noise_level],
+				examples_per_page=3,
+				label='Multiviews Examples',
+			)
+
+			gr.Markdown("*Don't have multi-view images on hand but want to generate your own multi-viwes? Generate them in the `Generate Multi Views (LGM)` secction above.*")
+
+	gr.Markdown(article)
+
+	button_gen_mv.click(
+		gen_mv, 
+		inputs=[ref_image, ref_text], 
+		outputs=[input_image_0, input_image_1, input_image_2, input_image_3, enhancer_text, enhancer_noise_level]
+	)
+
+	button_gen_3d.click(
+		gen_3d,
+		inputs=[input_image_0, input_image_1, input_image_2, input_image_3, elevation, original_video_path, original_ply_path],
+		outputs=[input_3d_video, button_download_3d]
+	).success(
+        lambda: gr.Button(interactive=True),
+        outputs=[button_download_3d],
+    )
+
+	enhanced_button_gen_3d.click(
+		gen_3d,
+		inputs=[enhanced_image_0, enhanced_image_1, enhanced_image_2, enhanced_image_3, elevation, original_video_path, original_ply_path],
+		outputs=[enhanced_3d_video, enhanced_button_download_3d]
+	).success(
+        lambda: gr.Button(interactive=True),
+        outputs=[enhanced_button_download_3d],
+    )
+
+	button_enhance.click(
+		enhance,
+		inputs=[input_image_0, input_image_1, input_image_2, input_image_3, enhancer_text, elevation, enhancer_noise_level, cfg_scale, steps, seed, color_shift],
+		outputs=[enhanced_image_0, enhanced_image_1, enhanced_image_2, enhanced_image_3]
+	).success(
+		gen_3d,
+		inputs=[input_image_0, input_image_1, input_image_2, input_image_3, elevation, original_video_path, original_ply_path],
+		outputs=[input_3d_video, button_download_3d]
+	).success(
+        lambda: gr.Button(interactive=True),
+        outputs=[button_download_3d],
+    ).success(
+		gen_3d,
+		inputs=[enhanced_image_0, enhanced_image_1, enhanced_image_2, enhanced_image_3, elevation, enhanced_video_path, enhanced_ply_path],
+		outputs=[enhanced_3d_video, enhanced_button_download_3d]
+	).success(
+        lambda: gr.Button(interactive=True),
+        outputs=[enhanced_button_download_3d],
+    )
+
+demo.launch()