# 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 logging import numpy as np import os import torch from PIL import Image from typing import Union, Optional from pathlib import Path from .differentiable_renderer.mesh_render import MeshRender from .utils.dehighlight_utils import Light_Shadow_Remover from .utils.multiview_utils import Multiview_Diffusion_Net # from .utils.imagesuper_utils import Image_Super_Net from .utils.uv_warp_utils import mesh_uv_wrap logger = logging.getLogger(__name__) # ------------------------------------------- # Device Selection (Global clean handling) # ------------------------------------------- def get_best_device(): if torch.cuda.is_available(): return "cuda" if torch.backends.mps.is_available(): return "mps" return "cpu" class Hunyuan3DTexGenConfig: def __init__(self, light_remover_ckpt_path, multiview_ckpt_path): # Old: self.device = 'cuda' self.device = get_best_device() self.light_remover_ckpt_path = light_remover_ckpt_path self.multiview_ckpt_path = multiview_ckpt_path self.candidate_camera_azims = [0, 90, 180, 270, 0, 180] self.candidate_camera_elevs = [0, 0, 0, 0, 90, -90] self.candidate_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05] self.render_size = 2048 self.texture_size = 2048 self.bake_exp = 4 self.merge_method = 'fast' class Hunyuan3DPaintPipeline: @classmethod def from_pretrained(cls, model_path): original_model_path = model_path print(f"原始路径 original_model_path: {model_path}") if not os.path.exists(model_path): print(f"不存在原始路径: {model_path}") base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') model_path = os.path.expanduser(os.path.join(base_dir, model_path)) delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') multiview_model_path = os.path.join(model_path, 'hunyuan3d-paint-v2-0') if not os.path.exists(delight_model_path) or not os.path.exists(multiview_model_path): try: import huggingface_hub model_path = huggingface_hub.snapshot_download( repo_id=original_model_path, allow_patterns=["hunyuan3d-delight-v2-0/*"] ) model_path = huggingface_hub.snapshot_download( repo_id=original_model_path, allow_patterns=["hunyuan3d-paint-v2-0/*"] ) delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') multiview_model_path = os.path.join(model_path, 'hunyuan3d-paint-v2-0') return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path)) except Exception as e: print("构造 Hunyuan3DPaintPipeline 实例时出错：", e) raise else: return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path)) raise FileNotFoundError(f"Model path {original_model_path} not found and Hub download failed.") def __init__(self, config): self.config = config self.models = {} self.render = MeshRender( default_resolution=self.config.render_size, texture_size=self.config.texture_size ) self.load_models() # ------------------------------------------- # Load Models — Dynamic CUDA handling # ------------------------------------------- def load_models(self): # Originally forced CUDA: # torch.cuda.empty_cache() if torch.cuda.is_available(): torch.cuda.empty_cache() self.models['delight_model'] = Light_Shadow_Remover(self.config) self.models['multiview_model'] = Multiview_Diffusion_Net(self.config) # self.models['super_model'] = Image_Super_Net(self.config) def enable_model_cpu_offload( self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = None ): if device is None: device = self.config.device if hasattr(self.models['delight_model'], "pipeline"): self.models['delight_model'].pipeline.enable_model_cpu_offload( gpu_id=gpu_id, device=device ) if hasattr(self.models['multiview_model'], "pipeline"): self.models['multiview_model'].pipeline.enable_model_cpu_offload( gpu_id=gpu_id, device=device ) # ------------------------------------------- # Rendering functions unchanged # ------------------------------------------- def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True): normal_maps = [] for elev, azim in zip(camera_elevs, camera_azims): normal_map = self.render.render_normal( elev, azim, use_abs_coor=use_abs_coor, return_type='pl') normal_maps.append(normal_map) return normal_maps def render_position_multiview(self, camera_elevs, camera_azims): position_maps = [] for elev, azim in zip(camera_elevs, camera_azims): position_map = self.render.render_position( elev, azim, return_type='pl') position_maps.append(position_map) return position_maps def bake_from_multiview(self, views, camera_elevs, camera_azims, view_weights, method='graphcut'): project_textures, project_weighted_cos_maps = [], [] project_boundary_maps = [] for view, camera_elev, camera_azim, weight in zip( views, camera_elevs, camera_azims, view_weights ): project_texture, project_cos_map, project_boundary_map = self.render.back_project( view, camera_elev, camera_azim ) project_cos_map = weight * (project_cos_map ** self.config.bake_exp) project_textures.append(project_texture) project_weighted_cos_maps.append(project_cos_map) project_boundary_maps.append(project_boundary_map) if method == 'fast': texture, ori_trust_map = self.render.fast_bake_texture( project_textures, project_weighted_cos_maps) else: raise f'no method {method}' return texture, ori_trust_map > 1E-8 def texture_inpaint(self, texture, mask): texture_np = self.render.uv_inpaint(texture, mask) texture = torch.tensor(texture_np / 255).float().to(texture.device) return texture def recenter_image(self, image, border_ratio=0.2): if image.mode == 'RGB': return image elif image.mode == 'L': return image.convert('RGB') alpha = np.array(image)[:, :, 3] non_zero = np.argwhere(alpha > 0) if non_zero.size == 0: raise ValueError("Image fully transparent") min_row, min_col = non_zero.min(axis=0) max_row, max_col = non_zero.max(axis=0) cropped = image.crop((min_col, min_row, max_col + 1, max_row + 1)) w, h = cropped.size bw = int(w * border_ratio) bh = int(h * border_ratio) new_w = w + 2 * bw new_h = h + 2 * bh sq = max(new_w, new_h) new_img = Image.new('RGBA', (sq, sq), (255, 255, 255, 0)) new_img.paste(cropped, ((sq - new_w) // 2 + bw, (sq - new_h) // 2 + bh)) return new_img @torch.no_grad() def __call__(self, mesh, image): if isinstance(image, str): image_prompt = Image.open(image) else: image_prompt = image image_prompt = self.recenter_image(image_prompt) # delight image_prompt = self.models['delight_model'](image_prompt) mesh = mesh_uv_wrap(mesh) self.render.load_mesh(mesh) elevs = self.config.candidate_camera_elevs azims = self.config.candidate_camera_azims weights = self.config.candidate_view_weights normal_maps = self.render_normal_multiview(elevs, azims) position_maps = self.render_position_multiview(elevs, azims) camera_info = [ (((azim // 30) + 9) % 12) // {-20: 1, 0: 1, 20: 1, -90: 3, 90: 3}[elev] + {-20: 0, 0: 12, 20: 24, -90: 36, 90: 40}[elev] for azim, elev in zip(azims, elevs) ] multiviews = self.models['multiview_model']( image_prompt, normal_maps + position_maps, camera_info ) for i in range(len(multiviews)): multiviews[i] = multiviews[i].resize( (self.config.render_size, self.config.render_size) ) texture, mask = self.bake_from_multiview( multiviews, elevs, azims, weights, method=self.config.merge_method ) mask_np = (mask.squeeze(-1).cpu().numpy() * 255).astype(np.uint8) texture = self.texture_inpaint(texture, mask_np) self.render.set_texture(texture) return self.render.save_mesh()