# Open Source Model Licensed under the Apache License Version 2.0 and Other Licenses of the Third-Party Components therein: # The below Model in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. # Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. # The below software and/or models in this distribution may have been # modified by THL A29 Limited ("Tencent Modifications"). # All Tencent Modifications are Copyright (C) THL A29 Limited. # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os import math import time import torch import numpy as np from tqdm import tqdm from PIL import Image, ImageSequence from omegaconf import OmegaConf from torchvision import transforms from safetensors.torch import save_file, load_file from .ldm.util import instantiate_from_config from .ldm.vis_util import render class MV23DPredictor(object): def __init__(self, ckpt_path, cfg_path, elevation=15, number_view=60, render_size=256, device="cuda:0") -> None: self.device = device self.elevation = elevation self.number_view = number_view self.render_size = render_size self.elevation_list = [0, 0, 0, 0, 0, 0, 0] self.azimuth_list = [0, 60, 120, 180, 240, 300, 0] st = time.time() self.model = self.init_model(ckpt_path, cfg_path) print(f"=====> mv23d model init time: {time.time() - st}") self.input_view_transform = transforms.Compose([ transforms.Resize(504, interpolation=Image.BICUBIC), transforms.ToTensor(), ]) self.final_input_view_transform = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) def init_model(self, ckpt_path, cfg_path): config = OmegaConf.load(cfg_path) model = instantiate_from_config(config.model) weights = load_file("./weights/svrm/svrm.safetensors") model.load_state_dict(weights) model.to(self.device) model = model.eval() model.render.half() print(f'Load model successfully') return model def create_camera_to_world_matrix(self, elevation, azimuth, cam_dis=1.5): # elevation azimuth are radians # Convert elevation and azimuth angles to Cartesian coordinates on a unit sphere x = np.cos(elevation) * np.cos(azimuth) y = np.cos(elevation) * np.sin(azimuth) z = np.sin(elevation) # Calculate camera position, target, and up vectors camera_pos = np.array([x, y, z]) * cam_dis target = np.array([0, 0, 0]) up = np.array([0, 0, 1]) # Construct view matrix forward = target - camera_pos forward /= np.linalg.norm(forward) right = np.cross(forward, up) right /= np.linalg.norm(right) new_up = np.cross(right, forward) new_up /= np.linalg.norm(new_up) cam2world = np.eye(4) cam2world[:3, :3] = np.array([right, new_up, -forward]).T cam2world[:3, 3] = camera_pos return cam2world def refine_mask(self, mask, k=16): mask /= 255.0 boder_mask = (mask >= -math.pi / 2.0 / k + 0.5) & (mask <= math.pi / 2.0 / k + 0.5) mask[boder_mask] = 0.5 * np.sin(k * (mask[boder_mask] - 0.5)) + 0.5 mask[mask < -math.pi / 2.0 / k + 0.5] = 0.0 mask[mask > math.pi / 2.0 / k + 0.5] = 1.0 return (mask * 255.0).astype(np.uint8) def load_images_and_cameras(self, input_imgs, elevation_list, azimuth_list): input_image_list = [] input_cam_list = [] for input_view_image, elevation, azimuth in zip(input_imgs, elevation_list, azimuth_list): input_view_image = self.input_view_transform(input_view_image) input_image_list.append(input_view_image) input_view_cam_pos = self.create_camera_to_world_matrix(np.radians(elevation), np.radians(azimuth)) input_view_cam_intrinsic = np.array([35. / 32, 35. /32, 0.5, 0.5]) input_view_cam = torch.from_numpy( np.concatenate([input_view_cam_pos.reshape(-1), input_view_cam_intrinsic], 0) ).float() input_cam_list.append(input_view_cam) pixels_input = torch.stack(input_image_list, dim=0) input_images = self.final_input_view_transform(pixels_input) input_cams = torch.stack(input_cam_list, dim=0) return input_images, input_cams def load_data(self, intput_imgs): assert (6+1) == len(intput_imgs) input_images, input_cams = self.load_images_and_cameras(intput_imgs, self.elevation_list, self.azimuth_list) input_cams[-1, :] = 0 # for user input view data = {} data["input_view"] = input_images.unsqueeze(0).to(self.device) # 1 4 3 512 512 data["input_view_cam"] = input_cams.unsqueeze(0).to(self.device) # 1 4 20 return data @torch.no_grad() def predict( self, intput_imgs, save_dir = "outputs/", image_input = None, target_face_count = 10000, do_texture_mapping = True, ): os.makedirs(save_dir, exist_ok=True) print(save_dir) with torch.cuda.amp.autocast(): self.model.export_mesh_with_uv( data = self.load_data(intput_imgs), out_dir = save_dir, target_face_count = target_face_count, do_texture_mapping = do_texture_mapping )