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 -> (m h) (n w) c', n=3, m=2) show_image = Image.fromarray(show_image.numpy()) return z123_image, show_image @spaces.GPU def make3d(input_image, sample_steps, sample_seed): cuda_path = find_cuda() if cuda_path: print(f"CUDA installation found at: {cuda_path}") else: print("CUDA installation not found") global model if IS_FLEXICUBES: model.init_flexicubes_geometry(device, use_renderer=False) model = model.eval() images, show_images = generate_mvs(input_image, sample_steps, sample_seed) 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=2.5).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[:, [0, 2, 1]] vertices[:, -1] *= -1 save_obj(vertices, faces, vertex_colors, mesh_fpath) print(f"Mesh saved to {mesh_fpath}") return mesh_fpath, show_images _HEADER_ = '''

Official 🤗 Gradio Demo

InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

''' _LINKS_ = '''

Code is available at GitHub

Report is available at ArXiv

''' _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 (Try a different value if the result is unsatisfying)", 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, ) gr.Markdown(_LINKS_) gr.Markdown(_CITE_) submit.click(fn=check_input_image, inputs=[input_image]).success( fn=preprocess, inputs=[input_image, do_remove_background], outputs=[processed_image], ).success( fn=make3d, inputs=[processed_image, sample_steps, sample_seed], outputs=[output_model_obj, mv_show_images] ) demo.launch()