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| 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> | |
|   <a href='https://Luo-Yihang.github.io/projects/3DEnhancer'>[Project Page]</a> | |
|   <a href='https://github.com/Luo-Yihang/3DEnhancer'>[Code]</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.<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! | |
| [](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>luo_yihang@outlook.com</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() | |
| 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 | |
| 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 | |
| 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(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() |