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Running
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Zero
| import spaces | |
| import os | |
| import imageio | |
| import numpy as np | |
| import torch | |
| import rembg | |
| from PIL import Image | |
| from torchvision.transforms import v2 | |
| from pytorch_lightning import seed_everything | |
| from omegaconf import OmegaConf | |
| from einops import rearrange, repeat | |
| from tqdm import tqdm | |
| from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler | |
| from src.utils.train_util import instantiate_from_config | |
| from src.utils.camera_util import ( | |
| FOV_to_intrinsics, | |
| get_zero123plus_input_cameras, | |
| get_circular_camera_poses, | |
| ) | |
| from src.utils.mesh_util import save_obj | |
| from src.utils.infer_util import remove_background, resize_foreground, images_to_video | |
| import tempfile | |
| from functools import partial | |
| from huggingface_hub import hf_hub_download | |
| import gradio as gr | |
| def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False): | |
| """ | |
| Get the rendering camera parameters. | |
| """ | |
| c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation) | |
| if is_flexicubes: | |
| cameras = torch.linalg.inv(c2ws) | |
| cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1) | |
| else: | |
| extrinsics = c2ws.flatten(-2) | |
| intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2) | |
| cameras = torch.cat([extrinsics, intrinsics], dim=-1) | |
| cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1) | |
| return cameras | |
| def images_to_video(images, output_path, fps=30): | |
| # images: (N, C, H, W) | |
| os.makedirs(os.path.dirname(output_path), exist_ok=True) | |
| frames = [] | |
| for i in range(images.shape[0]): | |
| frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255) | |
| assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \ | |
| f"Frame shape mismatch: {frame.shape} vs {images.shape}" | |
| assert frame.min() >= 0 and frame.max() <= 255, \ | |
| f"Frame value out of range: {frame.min()} ~ {frame.max()}" | |
| frames.append(frame) | |
| imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264') | |
| ############################################################################### | |
| # Configuration. | |
| ############################################################################### | |
| import shutil | |
| def find_cuda(): | |
| # Check if CUDA_HOME or CUDA_PATH environment variables are set | |
| cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH') | |
| if cuda_home and os.path.exists(cuda_home): | |
| return cuda_home | |
| # Search for the nvcc executable in the system's PATH | |
| nvcc_path = shutil.which('nvcc') | |
| if nvcc_path: | |
| # Remove the 'bin/nvcc' part to get the CUDA installation path | |
| cuda_path = os.path.dirname(os.path.dirname(nvcc_path)) | |
| return cuda_path | |
| return None | |
| cuda_path = find_cuda() | |
| if cuda_path: | |
| print(f"CUDA installation found at: {cuda_path}") | |
| else: | |
| print("CUDA installation not found") | |
| config_path = 'configs/instant-mesh-large.yaml' | |
| config = OmegaConf.load(config_path) | |
| config_name = os.path.basename(config_path).replace('.yaml', '') | |
| model_config = config.model_config | |
| infer_config = config.infer_config | |
| IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False | |
| device = torch.device('cuda') | |
| # load diffusion model | |
| print('Loading diffusion model ...') | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| "sudo-ai/zero123plus-v1.2", | |
| custom_pipeline="zero123plus", | |
| torch_dtype=torch.float16, | |
| ) | |
| pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( | |
| pipeline.scheduler.config, timestep_spacing='trailing' | |
| ) | |
| # load custom white-background UNet | |
| unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model") | |
| state_dict = torch.load(unet_ckpt_path, map_location='cpu') | |
| pipeline.unet.load_state_dict(state_dict, strict=True) | |
| pipeline = pipeline.to(device) | |
| # load reconstruction model | |
| print('Loading reconstruction model ...') | |
| model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model") | |
| model = instantiate_from_config(model_config) | |
| state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict'] | |
| state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k} | |
| model.load_state_dict(state_dict, strict=True) | |
| model = model.to(device) | |
| print('Loading Finished!') | |
| def check_input_image(input_image): | |
| if input_image is None: | |
| raise gr.Error("No image uploaded!") | |
| def preprocess(input_image, do_remove_background): | |
| rembg_session = rembg.new_session() if do_remove_background else None | |
| if do_remove_background: | |
| input_image = remove_background(input_image, rembg_session) | |
| input_image = resize_foreground(input_image, 0.85) | |
| return input_image | |
| def generate_mvs(input_image, sample_steps, sample_seed): | |
| seed_everything(sample_seed) | |
| # sampling | |
| z123_image = pipeline( | |
| input_image, | |
| num_inference_steps=sample_steps | |
| ).images[0] | |
| show_image = np.asarray(z123_image, dtype=np.uint8) | |
| show_image = torch.from_numpy(show_image) # (960, 640, 3) | |
| show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2) | |
| show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3) | |
| show_image = Image.fromarray(show_image.numpy()) | |
| return z123_image, show_image | |
| def make3d(images): | |
| global model | |
| if IS_FLEXICUBES: | |
| model.init_flexicubes_geometry(device, use_renderer=False) | |
| model = model.eval() | |
| images = np.asarray(images, dtype=np.float32) / 255.0 | |
| images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() # (3, 960, 640) | |
| images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) # (6, 3, 320, 320) | |
| input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device) | |
| render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device) | |
| images = images.unsqueeze(0).to(device) | |
| images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1) | |
| mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name | |
| print(mesh_fpath) | |
| mesh_basename = os.path.basename(mesh_fpath).split('.')[0] | |
| mesh_dirname = os.path.dirname(mesh_fpath) | |
| video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4") | |
| with torch.no_grad(): | |
| # get triplane | |
| planes = model.forward_planes(images, input_cameras) | |
| # # get video | |
| # chunk_size = 20 if IS_FLEXICUBES else 1 | |
| # render_size = 384 | |
| # frames = [] | |
| # for i in tqdm(range(0, render_cameras.shape[1], chunk_size)): | |
| # if IS_FLEXICUBES: | |
| # frame = model.forward_geometry( | |
| # planes, | |
| # render_cameras[:, i:i+chunk_size], | |
| # render_size=render_size, | |
| # )['img'] | |
| # else: | |
| # frame = model.synthesizer( | |
| # planes, | |
| # cameras=render_cameras[:, i:i+chunk_size], | |
| # render_size=render_size, | |
| # )['images_rgb'] | |
| # frames.append(frame) | |
| # frames = torch.cat(frames, dim=1) | |
| # images_to_video( | |
| # frames[0], | |
| # video_fpath, | |
| # fps=30, | |
| # ) | |
| # print(f"Video saved to {video_fpath}") | |
| # get mesh | |
| mesh_out = model.extract_mesh( | |
| planes, | |
| use_texture_map=False, | |
| **infer_config, | |
| ) | |
| vertices, faces, vertex_colors = mesh_out | |
| vertices = vertices[:, [1, 2, 0]] | |
| vertices[:, -1] *= -1 | |
| faces = faces[:, [2, 1, 0]] | |
| save_obj(vertices, faces, vertex_colors, mesh_fpath) | |
| print(f"Mesh saved to {mesh_fpath}") | |
| return mesh_fpath | |
| _HEADER_ = ''' | |
| <h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2> | |
| ''' | |
| _LINKS_ = ''' | |
| <h3>Code is available at <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a></h3> | |
| <h3>Report is available at <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a></h3> | |
| ''' | |
| _CITE_ = r""" | |
| ```bibtex | |
| @article{xu2024instantmesh, | |
| title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models}, | |
| author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying}, | |
| journal={arXiv preprint arXiv:2404.07191}, | |
| year={2024} | |
| } | |
| ``` | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown(_HEADER_) | |
| with gr.Row(variant="panel"): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_image = gr.Image( | |
| label="Input Image", | |
| image_mode="RGBA", | |
| sources="upload", | |
| #width=256, | |
| #height=256, | |
| type="pil", | |
| elem_id="content_image", | |
| ) | |
| processed_image = gr.Image( | |
| label="Processed Image", | |
| image_mode="RGBA", | |
| #width=256, | |
| #height=256, | |
| type="pil", | |
| interactive=False | |
| ) | |
| with gr.Row(): | |
| with gr.Group(): | |
| do_remove_background = gr.Checkbox( | |
| label="Remove Background", value=True | |
| ) | |
| sample_seed = gr.Number(value=42, label="Seed Value", precision=0) | |
| sample_steps = gr.Slider( | |
| label="Sample Steps", | |
| minimum=30, | |
| maximum=75, | |
| value=75, | |
| step=5 | |
| ) | |
| with gr.Row(): | |
| submit = gr.Button("Generate", elem_id="generate", variant="primary") | |
| with gr.Row(variant="panel"): | |
| gr.Examples( | |
| examples=[ | |
| os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples")) | |
| ], | |
| inputs=[input_image], | |
| label="Examples", | |
| cache_examples=False, | |
| examples_per_page=12 | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| mv_show_images = gr.Image( | |
| label="Generated Multi-views", | |
| type="pil", | |
| width=379, | |
| interactive=False | |
| ) | |
| # with gr.Column(): | |
| # output_video = gr.Video( | |
| # label="video", format="mp4", | |
| # width=379, | |
| # autoplay=True, | |
| # interactive=False | |
| # ) | |
| with gr.Row(): | |
| output_model_obj = gr.Model3D( | |
| label="Output Model (OBJ Format)", | |
| interactive=False, | |
| ) | |
| with gr.Row(): | |
| gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''') | |
| gr.Markdown(_LINKS_) | |
| gr.Markdown(_CITE_) | |
| mv_images = gr.State() | |
| submit.click(fn=check_input_image, inputs=[input_image]).success( | |
| fn=preprocess, | |
| inputs=[input_image, do_remove_background], | |
| outputs=[processed_image], | |
| ).success( | |
| fn=generate_mvs, | |
| inputs=[processed_image, sample_steps, sample_seed], | |
| outputs=[mv_images, mv_show_images] | |
| ).success( | |
| fn=make3d, | |
| inputs=[mv_images], | |
| outputs=[output_model_obj] | |
| ) | |
| demo.launch() |