import spaces import os os.environ["CXX"] = os.popen("which g++").read().strip() os.environ["CC"] = os.popen("which gcc").read().strip() os.environ['CUDA_LAUNCH_BLOCKING']="1" os.environ['TORCH_USE_CUDA_DSA'] = "1" import nvdiffrast.torch as dr 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 import glm from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler from src.data.objaverse import load_mipmap from src.utils import render_utils 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, save_glb from src.utils.infer_util import remove_background, resize_foreground, images_to_video import subprocess import sys import tempfile from huggingface_hub import hf_hub_download def install_cuda_toolkit(): CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run" # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run" # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_linux.run" print(f"==> install cuda {CUDA_TOOLKIT_URL}") CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL) subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE]) subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE]) subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"]) os.environ["CUDA_HOME"] = "/usr/local/cuda" os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"]) os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % ( os.environ["CUDA_HOME"], "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"], ) # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6" print("==> finfish install") 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 install_cuda_toolkit() cuda_path = find_cuda() if cuda_path: print(f"CUDA installation found at: {cuda_path}") else: print("CUDA installation not found") # import os # # 确保 CUDA_HOME 设置正确 # os.environ["CUDA_HOME"] = "/usr/local/cuda" # os.environ["PATH"] += os.pathsep + os.path.join(os.environ["CUDA_HOME"], "bin") # os.environ["LD_LIBRARY_PATH"] = os.environ.get("LD_LIBRARY_PATH", "") + os.pathsep + os.path.join(os.environ["CUDA_HOME"], "lib64") GLCTX = dr.RasterizeCudaContext() print(f"GPU: {torch.cuda.is_available()}") if torch.cuda.is_available() and torch.cuda.device_count() >= 2: device0 = torch.device('cuda:0') device1 = torch.device('cuda:0') else: device0 = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device1 = device0 # Define the cache directory for model files model_cache_dir = './ckpts/' os.makedirs(model_cache_dir, exist_ok=True) @spaces.GPU def get_render_cameras(batch_size=1, M=120, radius=4.0, elevation=20.0, is_flexicubes=False, fov=50): """ Get the rendering camera parameters. """ train_res = [512, 512] cam_near_far = [0.1, 1000.0] fovy = np.deg2rad(fov) proj_mtx = render_utils.perspective(fovy, train_res[1] / train_res[0], cam_near_far[0], cam_near_far[1]) all_mv = [] all_mvp = [] all_campos = [] if isinstance(elevation, tuple): elevation_0 = np.deg2rad(elevation[0]) elevation_1 = np.deg2rad(elevation[1]) for i in range(M//2): azimuth = 2 * np.pi * i / (M // 2) z = radius * np.cos(azimuth) * np.sin(elevation_0) x = radius * np.sin(azimuth) * np.sin(elevation_0) y = radius * np.cos(elevation_0) eye = glm.vec3(x, y, z) at = glm.vec3(0.0, 0.0, 0.0) up = glm.vec3(0.0, 1.0, 0.0) view_matrix = glm.lookAt(eye, at, up) mv = torch.from_numpy(np.array(view_matrix)) mvp = proj_mtx @ (mv) #w2c campos = torch.linalg.inv(mv)[:3, 3] all_mv.append(mv[None, ...].cuda()) all_mvp.append(mvp[None, ...].cuda()) all_campos.append(campos[None, ...].cuda()) for i in range(M//2): azimuth = 2 * np.pi * i / (M // 2) z = radius * np.cos(azimuth) * np.sin(elevation_1) x = radius * np.sin(azimuth) * np.sin(elevation_1) y = radius * np.cos(elevation_1) eye = glm.vec3(x, y, z) at = glm.vec3(0.0, 0.0, 0.0) up = glm.vec3(0.0, 1.0, 0.0) view_matrix = glm.lookAt(eye, at, up) mv = torch.from_numpy(np.array(view_matrix)) mvp = proj_mtx @ (mv) #w2c campos = torch.linalg.inv(mv)[:3, 3] all_mv.append(mv[None, ...].cuda()) all_mvp.append(mvp[None, ...].cuda()) all_campos.append(campos[None, ...].cuda()) else: # elevation = 90 - elevation for i in range(M): azimuth = 2 * np.pi * i / M z = radius * np.cos(azimuth) * np.sin(elevation) x = radius * np.sin(azimuth) * np.sin(elevation) y = radius * np.cos(elevation) eye = glm.vec3(x, y, z) at = glm.vec3(0.0, 0.0, 0.0) up = glm.vec3(0.0, 1.0, 0.0) view_matrix = glm.lookAt(eye, at, up) mv = torch.from_numpy(np.array(view_matrix)) mvp = proj_mtx @ (mv) #w2c campos = torch.linalg.inv(mv)[:3, 3] all_mv.append(mv[None, ...].cuda()) all_mvp.append(mvp[None, ...].cuda()) all_campos.append(campos[None, ...].cuda()) all_mv = torch.stack(all_mv, dim=0).unsqueeze(0).squeeze(2) all_mvp = torch.stack(all_mvp, dim=0).unsqueeze(0).squeeze(2) all_campos = torch.stack(all_campos, dim=0).unsqueeze(0).squeeze(2) return all_mv, all_mvp, all_campos @spaces.GPU def render_frames(model, planes, render_cameras, camera_pos, env, materials, render_size=512, chunk_size=1, is_flexicubes=False): """ Render frames from triplanes. """ frames = [] albedos = [] pbr_spec_lights = [] pbr_diffuse_lights = [] normals = [] alphas = [] for i in tqdm(range(0, render_cameras.shape[1], chunk_size)): if is_flexicubes: out = model.forward_geometry( planes, render_cameras[:, i:i+chunk_size], camera_pos[:, i:i+chunk_size], [[env]*chunk_size], [[materials]*chunk_size], render_size=render_size, ) frame = out['pbr_img'] albedo = out['albedo'] pbr_spec_light = out['pbr_spec_light'] pbr_diffuse_light = out['pbr_diffuse_light'] normal = out['normal'] alpha = out['mask'] else: frame = model.forward_synthesizer( planes, render_cameras[i], render_size=render_size, )['images_rgb'] frames.append(frame) albedos.append(albedo) pbr_spec_lights.append(pbr_spec_light) pbr_diffuse_lights.append(pbr_diffuse_light) normals.append(normal) alphas.append(alpha) frames = torch.cat(frames, dim=1)[0] # we suppose batch size is always 1 alphas = torch.cat(alphas, dim=1)[0] albedos = torch.cat(albedos, dim=1)[0] pbr_spec_lights = torch.cat(pbr_spec_lights, dim=1)[0] pbr_diffuse_lights = torch.cat(pbr_diffuse_lights, dim=1)[0] normals = torch.cat(normals, dim=0).permute(0,3,1,2)[:,:3] return frames, albedos, pbr_spec_lights, pbr_diffuse_lights, normals, alphas 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. ############################################################################### seed_everything(0) config_path = 'configs/PRM_inference.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 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, cache_dir=model_cache_dir ) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( pipeline.scheduler.config, timestep_spacing='trailing' ) # load custom white-background UNet print('Loading custom white-background unet ...') if os.path.exists(infer_config.unet_path): unet_ckpt_path = infer_config.unet_path else: unet_ckpt_path = hf_hub_download(repo_id="LTT/xxx", 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 = instantiate_from_config(model_config) if os.path.exists(infer_config.model_path): model_ckpt_path = infer_config.model_path else: model_ckpt_path = hf_hub_download(repo_id="LTT/xxx", filename="final_ckpt.ckpt", repo_type="model") 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.')} model.load_state_dict(state_dict, strict=True) model = model.to(device) if IS_FLEXICUBES: model.init_flexicubes_geometry(device, fovy=30.0, use_renderer=False) model = model.eval() 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 @spaces.GPU def generate_mvs(input_image, sample_steps, sample_seed): seed_everything(sample_seed) # sampling generator = torch.Generator(device=device0) z123_image = pipeline( input_image, num_inference_steps=sample_steps, generator=generator, ).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 make_mesh(mesh_fpath, planes): mesh_basename = os.path.basename(mesh_fpath).split('.')[0] mesh_dirname = os.path.dirname(mesh_fpath) mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb") with torch.no_grad(): # 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]] save_glb(vertices, faces, vertex_colors, mesh_glb_fpath) save_obj(vertices, faces, vertex_colors, mesh_fpath) print(f"Mesh saved to {mesh_fpath}") return mesh_fpath, mesh_glb_fpath @spaces.GPU def make3d(images): 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=3.2, fov=30).to(device).to(device1) all_mv, all_mvp, all_campos = get_render_cameras( batch_size=1, M=240, radius=4.5, elevation=(90, 60.0), is_flexicubes=IS_FLEXICUBES, fov=30 ) images = images.unsqueeze(0).to(device1) images = v2.functional.resize(images, (512, 512), 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") ENV = load_mipmap("env_mipmap/6") materials = (0.0,0.9) 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 = 512 frames = [] frames, albedos, pbr_spec_lights, pbr_diffuse_lights, normals, alphas = render_frames( model, planes, render_cameras=all_mvp, camera_pos=all_campos, env=ENV, materials=materials, render_size=render_size, chunk_size=chunk_size, is_flexicubes=IS_FLEXICUBES, ) normals = (torch.nn.functional.normalize(normals) + 1) / 2 normals = normals * alphas + (1-alphas) all_frames = torch.cat([frames, albedos, pbr_spec_lights, pbr_diffuse_lights, normals], dim=3) images_to_video( all_frames, video_fpath, fps=30, ) print(f"Video saved to {video_fpath}") mesh_fpath, mesh_glb_fpath = make_mesh(mesh_fpath, planes) # return mesh_fpath, mesh_glb_fpath return video_fpath, mesh_fpath, mesh_glb_fpath import gradio as gr _HEADER_ = '''

Official 🤗 Gradio Demo

PRM: Photometric Stereo based Large Reconstruction Model

**PRM** is a feed-forward framework for high-quality 3D mesh generation with fine-grained local details from a single image. Code: GitHub. ''' _CITE_ = r""" If PRM is helpful, please help to ⭐ the Github Repo. Thanks! --- 📝 **Citation** If you find our work useful for your research or applications, please cite using this bibtex: ```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} } ``` 📋 **License** Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/datasets/choosealicense/licenses/resolve/main/markdown/apache-2.0.md) for details. 📧 **Contact** If you have any questions, feel free to open a discussion or contact us at jlin695@connect.hkust-gz.edu.cn. """ 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=100, 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", examples_per_page=20 ) 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(): with gr.Column(): output_video = gr.Video( label="video", format="mp4", width=768, autoplay=True, interactive=False ) with gr.Row(): with gr.Tab("OBJ"): output_model_obj = gr.Model3D( label="Output Model (OBJ Format)", #width=768, interactive=False, ) gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.") with gr.Tab("GLB"): output_model_glb = gr.Model3D( label="Output Model (GLB Format)", #width=768, interactive=False, ) gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.") with gr.Row(): gr.Markdown('''Try a different seed value if the result is unsatisfying (Default: 42).''') 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, output_model_glb] outputs=[output_video, output_model_obj, output_model_glb] ) # demo.queue(max_size=10) demo.launch()