import os import torch import torchvision.transforms as transforms import torch.nn.functional as F from PIL import Image from pathlib import Path import numpy as np import json import trimesh from tqdm import tqdm from .hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline, FaceReducer, FloaterRemover, DegenerateFaceRemover from .hy3dgen.texgen.hunyuanpaint.unet.modules import UNet2DConditionModel, UNet2p5DConditionModel from .hy3dgen.texgen.hunyuanpaint.pipeline import HunyuanPaintPipeline from diffusers import AutoencoderKL from diffusers.schedulers import ( DDIMScheduler, PNDMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, HeunDiscreteScheduler, SASolverScheduler, DEISMultistepScheduler, LCMScheduler ) scheduler_mapping = { "DPM++": DPMSolverMultistepScheduler, "DPM++SDE": DPMSolverMultistepScheduler, "Euler": EulerDiscreteScheduler, "Euler A": EulerAncestralDiscreteScheduler, "PNDM": PNDMScheduler, "DDIM": DDIMScheduler, "SASolverScheduler": SASolverScheduler, "UniPCMultistepScheduler": UniPCMultistepScheduler, "HeunDiscreteScheduler": HeunDiscreteScheduler, "DEISMultistepScheduler": DEISMultistepScheduler, "LCMScheduler": LCMScheduler } available_schedulers = list(scheduler_mapping.keys()) from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from accelerate import init_empty_weights from accelerate.utils import set_module_tensor_to_device import folder_paths import comfy.model_management as mm from comfy.utils import load_torch_file, ProgressBar script_directory = os.path.dirname(os.path.abspath(__file__)) from .utils import log, print_memory class ComfyProgressCallback: def __init__(self, total_steps): self.pbar = ProgressBar(total_steps) def __call__(self, pipe, i, t, callback_kwargs): self.pbar.update(1) return { "latents": callback_kwargs["latents"], "prompt_embeds": callback_kwargs["prompt_embeds"], "negative_prompt_embeds": callback_kwargs["negative_prompt_embeds"] } class Hy3DTorchCompileSettings: @classmethod def INPUT_TYPES(s): return { "required": { "backend": (["inductor","cudagraphs"], {"default": "inductor"}), "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), "compile_transformer": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}), "compile_vae": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}), }, } RETURN_TYPES = ("HY3DCOMPILEARGS",) RETURN_NAMES = ("torch_compile_args",) FUNCTION = "loadmodel" CATEGORY = "HunyuanVideoWrapper" DESCRIPTION = "torch.compile settings, when connected to the model loader, torch.compile of the selected layers is attempted. Requires Triton and torch 2.5.0 is recommended" def loadmodel(self, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer, compile_vae): compile_args = { "backend": backend, "fullgraph": fullgraph, "mode": mode, "dynamic": dynamic, "dynamo_cache_size_limit": dynamo_cache_size_limit, "compile_transformer": compile_transformer, "compile_vae": compile_vae, } return (compile_args, ) #region Model loading class Hy3DModelLoader: @classmethod def INPUT_TYPES(s): return { "required": { "model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}), }, "optional": { "compile_args": ("HY3DCOMPILEARGS", {"tooltip": "torch.compile settings, when connected to the model loader, torch.compile of the selected models is attempted. Requires Triton and torch 2.5.0 is recommended"}), "attention_mode": (["sdpa", "sageattn"], {"default": "sdpa"}), } } RETURN_TYPES = ("HY3DMODEL", "HY3DVAE") RETURN_NAMES = ("pipeline", "vae") FUNCTION = "loadmodel" CATEGORY = "Hunyuan3DWrapper" def loadmodel(self, model, compile_args=None, attention_mode="sdpa"): device = mm.get_torch_device() offload_device=mm.unet_offload_device() config_path = os.path.join(script_directory, "configs", "dit_config.yaml") model_path = folder_paths.get_full_path("diffusion_models", model) pipe, vae = Hunyuan3DDiTFlowMatchingPipeline.from_single_file( ckpt_path=model_path, config_path=config_path, use_safetensors=True, device=device, offload_device=offload_device, compile_args=compile_args, attention_mode=attention_mode) return (pipe, vae,) class DownloadAndLoadHy3DDelightModel: @classmethod def INPUT_TYPES(s): return { "required": { "model": (["hunyuan3d-delight-v2-0"],), }, "optional": { "compile_args": ("HY3DCOMPILEARGS", {"tooltip": "torch.compile settings, when connected to the model loader, torch.compile of the selected models is attempted. Requires Triton and torch 2.5.0 is recommended"}), } } RETURN_TYPES = ("HY3DDIFFUSERSPIPE",) RETURN_NAMES = ("delight_pipe", ) FUNCTION = "loadmodel" CATEGORY = "Hunyuan3DWrapper" def loadmodel(self, model, compile_args=None): device = mm.get_torch_device() download_path = os.path.join(folder_paths.models_dir,"diffusers") model_path = os.path.join(download_path, model) if not os.path.exists(model_path): log.info(f"Downloading model to: {model_path}") from huggingface_hub import snapshot_download snapshot_download( repo_id="tencent/Hunyuan3D-2", allow_patterns=["*hunyuan3d-delight-v2-0*"], local_dir=download_path, local_dir_use_symlinks=False, ) from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler delight_pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( model_path, torch_dtype=torch.float16, safety_checker=None, ) delight_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(delight_pipe.scheduler.config) delight_pipe = delight_pipe.to(device, torch.float16) if compile_args is not None: torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"] if compile_args["compile_transformer"]: delight_pipe.unet = torch.compile(delight_pipe.unet) if compile_args["compile_vae"]: delight_pipe.vae = torch.compile(delight_pipe.vae) else: delight_pipe.enable_model_cpu_offload() return (delight_pipe,) class Hy3DDelightImage: @classmethod def INPUT_TYPES(s): return { "required": { "delight_pipe": ("HY3DDIFFUSERSPIPE",), "image": ("IMAGE", ), "steps": ("INT", {"default": 50, "min": 1}), "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16}), "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16}), "cfg_image": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "scheduler": ("NOISESCHEDULER",), } } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, delight_pipe, image, width, height, cfg_image, steps, seed, scheduler=None): device = mm.get_torch_device() offload_device = mm.unet_offload_device() if scheduler is not None: if not hasattr(self, "default_scheduler"): self.default_scheduler = delight_pipe.scheduler delight_pipe.scheduler = scheduler else: if hasattr(self, "default_scheduler"): delight_pipe.scheduler = self.default_scheduler image = image.permute(0, 3, 1, 2).to(device) image = delight_pipe( prompt="", image=image, generator=torch.manual_seed(seed), height=height, width=width, num_inference_steps=steps, image_guidance_scale=cfg_image, guidance_scale=1.0 if cfg_image == 1.0 else 1.01, #enable cfg for image, value doesn't matter as it do anything for text anyway output_type="pt", ).images[0] out_tensor = image.unsqueeze(0).permute(0, 2, 3, 1).cpu().float() return (out_tensor, ) class DownloadAndLoadHy3DPaintModel: @classmethod def INPUT_TYPES(s): return { "required": { "model": (["hunyuan3d-paint-v2-0"],), }, "optional": { "compile_args": ("HY3DCOMPILEARGS", {"tooltip": "torch.compile settings, when connected to the model loader, torch.compile of the selected models is attempted. Requires Triton and torch 2.5.0 is recommended"}), } } RETURN_TYPES = ("HY3DDIFFUSERSPIPE",) RETURN_NAMES = ("multiview_pipe", ) FUNCTION = "loadmodel" CATEGORY = "Hunyuan3DWrapper" def loadmodel(self, model, compile_args=None): device = mm.get_torch_device() offload_device = mm.unet_offload_device() download_path = os.path.join(folder_paths.models_dir,"diffusers") model_path = os.path.join(download_path, model) if not os.path.exists(model_path): log.info(f"Downloading model to: {model_path}") from huggingface_hub import snapshot_download snapshot_download( repo_id="tencent/Hunyuan3D-2", allow_patterns=[f"*{model}*"], ignore_patterns=["*diffusion_pytorch_model.bin"], local_dir=download_path, local_dir_use_symlinks=False, ) torch_dtype = torch.float16 config_path = os.path.join(model_path, 'unet', 'config.json') unet_ckpt_path_safetensors = os.path.join(model_path, 'unet','diffusion_pytorch_model.safetensors') unet_ckpt_path_bin = os.path.join(model_path, 'unet','diffusion_pytorch_model.bin') if not os.path.exists(config_path): raise FileNotFoundError(f"Config not found at {config_path}") with open(config_path, 'r', encoding='utf-8') as file: config = json.load(file) with init_empty_weights(): unet = UNet2DConditionModel(**config) unet = UNet2p5DConditionModel(unet) # Try loading safetensors first, fall back to .bin if os.path.exists(unet_ckpt_path_safetensors): import safetensors.torch unet_sd = safetensors.torch.load_file(unet_ckpt_path_safetensors) elif os.path.exists(unet_ckpt_path_bin): unet_sd = torch.load(unet_ckpt_path_bin, map_location='cpu', weights_only=True) else: raise FileNotFoundError(f"No checkpoint found at {unet_ckpt_path_safetensors} or {unet_ckpt_path_bin}") #unet.load_state_dict(unet_ckpt, strict=True) for name, param in unet.named_parameters(): set_module_tensor_to_device(unet, name, device=offload_device, dtype=torch_dtype, value=unet_sd[name]) vae = AutoencoderKL.from_pretrained(model_path, subfolder="vae", device=device, torch_dtype=torch_dtype) clip = CLIPTextModel.from_pretrained(model_path, subfolder="text_encoder", torch_dtype=torch_dtype) tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer") scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler") feature_extractor = CLIPImageProcessor.from_pretrained(model_path, subfolder="feature_extractor") pipeline = HunyuanPaintPipeline( unet=unet, vae = vae, text_encoder=clip, tokenizer=tokenizer, scheduler=scheduler, feature_extractor=feature_extractor, ) if compile_args is not None: pipeline.to(device) torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"] if compile_args["compile_transformer"]: pipeline.unet = torch.compile(pipeline.unet) if compile_args["compile_vae"]: pipeline.vae = torch.compile(pipeline.vae) else: pipeline.enable_model_cpu_offload() return (pipeline,) #region Texture class Hy3DCameraConfig: @classmethod def INPUT_TYPES(s): return { "required": { "camera_azimuths": ("STRING", {"default": "0, 90, 180, 270, 0, 180", "multiline": False}), "camera_elevations": ("STRING", {"default": "0, 0, 0, 0, 90, -90", "multiline": False}), "view_weights": ("STRING", {"default": "1, 0.1, 0.5, 0.1, 0.05, 0.05", "multiline": False}), "camera_distance": ("FLOAT", {"default": 1.45, "min": 0.1, "max": 10.0, "step": 0.001}), "ortho_scale": ("FLOAT", {"default": 1.2, "min": 0.1, "max": 10.0, "step": 0.001}), }, } RETURN_TYPES = ("HY3DCAMERA",) RETURN_NAMES = ("camera_config",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, camera_azimuths, camera_elevations, view_weights, camera_distance, ortho_scale): angles_list = list(map(int, camera_azimuths.replace(" ", "").split(','))) elevations_list = list(map(int, camera_elevations.replace(" ", "").split(','))) weights_list = list(map(float, view_weights.replace(" ", "").split(','))) camera_config = { "selected_camera_azims": angles_list, "selected_camera_elevs": elevations_list, "selected_view_weights": weights_list, "camera_distance": camera_distance, "ortho_scale": ortho_scale, } return (camera_config,) class Hy3DMeshUVWrap: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), }, } RETURN_TYPES = ("HY3DMESH", ) RETURN_NAMES = ("mesh", ) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh): from .hy3dgen.texgen.utils.uv_warp_utils import mesh_uv_wrap mesh = mesh_uv_wrap(mesh) return (mesh,) class Hy3DRenderMultiView: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "render_size": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}), "texture_size": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}), }, "optional": { "camera_config": ("HY3DCAMERA",), "normal_space": (["world", "tangent"], {"default": "world"}), } } RETURN_TYPES = ("IMAGE", "IMAGE", "MESHRENDER", "MASK",) RETURN_NAMES = ("normal_maps", "position_maps", "renderer", "masks") FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh, render_size, texture_size, camera_config=None, normal_space="world"): from .hy3dgen.texgen.differentiable_renderer.mesh_render import MeshRender if camera_config is None: selected_camera_azims = [0, 90, 180, 270, 0, 180] selected_camera_elevs = [0, 0, 0, 0, 90, -90] camera_distance = 1.45 ortho_scale = 1.2 else: selected_camera_azims = camera_config["selected_camera_azims"] selected_camera_elevs = camera_config["selected_camera_elevs"] camera_distance = camera_config["camera_distance"] ortho_scale = camera_config["ortho_scale"] self.render = MeshRender( default_resolution=render_size, texture_size=texture_size, camera_distance=camera_distance, ortho_scale=ortho_scale) self.render.load_mesh(mesh) if normal_space == "world": normal_maps, masks = self.render_normal_multiview( selected_camera_elevs, selected_camera_azims, use_abs_coor=True) normal_tensors = torch.stack(normal_maps, dim=0) mask_tensors = torch.cat(masks, dim=0) elif normal_space == "tangent": normal_maps, masks = self.render_normal_multiview( selected_camera_elevs, selected_camera_azims, bg_color=[0, 0, 0], use_abs_coor=False) normal_tensors = torch.stack(normal_maps, dim=0) normal_tensors = 2.0 * normal_tensors - 1.0 # Map [0,1] to [-1,1] normal_tensors = normal_tensors / (torch.norm(normal_tensors, dim=-1, keepdim=True) + 1e-6) # Remap axes for standard normal map convention image = torch.zeros_like(normal_tensors) image[..., 0] = normal_tensors[..., 0] # View right to R image[..., 1] = normal_tensors[..., 1] # View up to G image[..., 2] = -normal_tensors[..., 2] # View forward (negated) to B # Create background color background_color = torch.tensor([0.502, 0.502, 1.0], device=normal_tensors.device) #8080FF mask_tensors = torch.cat(masks, dim=0) # Blend rendered image with background normal_tensors = (image + 1) * 0.5 normal_tensors = normal_tensors * mask_tensors + background_color * (1 - mask_tensors) position_maps = self.render_position_multiview( selected_camera_elevs, selected_camera_azims) position_tensors = torch.stack(position_maps, dim=0) return (normal_tensors.cpu().float(), position_tensors.cpu().float(), self.render, mask_tensors.squeeze(-1).cpu().float(),) def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True, bg_color=[1, 1, 1]): normal_maps = [] masks = [] for elev, azim in zip(camera_elevs, camera_azims): normal_map, mask = self.render.render_normal( elev, azim, bg_color=bg_color, use_abs_coor=use_abs_coor, return_type='th') normal_maps.append(normal_map) masks.append(mask) return normal_maps, masks 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='th') position_maps.append(position_map) return position_maps class Hy3DRenderSingleView: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "render_type": (["normal", "depth"], {"default": "normal"}), "render_size": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}), "camera_type": (["orth", "perspective"], {"default": "orth"}), "camera_distance": ("FLOAT", {"default": 1.45, "min": 0.1, "max": 10.0, "step": 0.001}), "pan_x": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}), "pan_y": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}), "ortho_scale": ("FLOAT", {"default": 1.2, "min": 0.1, "max": 10.0, "step": 0.001}), "azimuth": ("FLOAT", {"default": 0, "min": -360, "max": 360, "step": 1}), "elevation": ("FLOAT", {"default": 0, "min": -360, "max": 360, "step": 1}), "bg_color": ("STRING", {"default": "128, 128, 255", "tooltip": "Color as RGB values in range 0-255, separated by commas."}), }, } RETURN_TYPES = ("IMAGE", "MASK",) RETURN_NAMES = ("image", "mask", ) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh, render_type, camera_type, ortho_scale, camera_distance, pan_x, pan_y, render_size, azimuth, elevation, bg_color): from .hy3dgen.texgen.differentiable_renderer.mesh_render import MeshRender bg_color = [int(x.strip())/255.0 for x in bg_color.split(",")] self.render = MeshRender( default_resolution=render_size, texture_size=1024, camera_distance=camera_distance, camera_type=camera_type, ortho_scale=ortho_scale, filter_mode='linear' ) self.render.load_mesh(mesh) if render_type == "normal": normals, mask = self.render.render_normal( elevation, azimuth, camera_distance=camera_distance, center=None, resolution=render_size, bg_color=[0, 0, 0], use_abs_coor=False, pan_x=pan_x, pan_y=pan_y ) normals = 2.0 * normals - 1.0 # Map [0,1] to [-1,1] normals = normals / (torch.norm(normals, dim=-1, keepdim=True) + 1e-6) # Remap axes for standard normal map convention image = torch.zeros_like(normals) image[..., 0] = normals[..., 0] # View right to R image[..., 1] = normals[..., 1] # View up to G image[..., 2] = -normals[..., 2] # View forward (negated) to B image = (image + 1) * 0.5 #mask = mask.cpu().float() masked_image = image * mask bg_color = torch.tensor(bg_color, dtype=torch.float32, device=image.device) bg = bg_color.view(1, 1, 3) * (1.0 - mask) final_image = masked_image + bg elif render_type == "depth": depth = self.render.render_depth( elevation, azimuth, camera_distance=camera_distance, center=None, resolution=render_size, pan_x=pan_x, pan_y=pan_y ) final_image = depth.unsqueeze(0).repeat(1, 1, 1, 3) return (final_image.cpu().float(), mask.squeeze(-1).cpu().float(),) class Hy3DRenderMultiViewDepth: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "render_size": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}), "texture_size": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 16}), }, "optional": { "camera_config": ("HY3DCAMERA",), } } RETURN_TYPES = ("IMAGE", "MASK",) RETURN_NAMES = ("depth_maps", "masks", ) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh, render_size, texture_size, camera_config=None): mm.unload_all_models() mm.soft_empty_cache() from .hy3dgen.texgen.differentiable_renderer.mesh_render import MeshRender if camera_config is None: selected_camera_azims = [0, 90, 180, 270, 0, 180] selected_camera_elevs = [0, 0, 0, 0, 90, -90] camera_distance = 1.45 ortho_scale = 1.2 else: selected_camera_azims = camera_config["selected_camera_azims"] selected_camera_elevs = camera_config["selected_camera_elevs"] camera_distance = camera_config["camera_distance"] ortho_scale = camera_config["ortho_scale"] self.render = MeshRender( default_resolution=render_size, texture_size=texture_size, camera_distance=camera_distance, ortho_scale=ortho_scale) self.render.load_mesh(mesh) depth_maps, masks = self.render_depth_multiview( selected_camera_elevs, selected_camera_azims) depth_tensors = torch.stack(depth_maps, dim=0) depth_tensors = depth_tensors.repeat(1, 1, 1, 3).cpu().float() masks = torch.cat(masks, dim=0).squeeze(-1).cpu().float() return (depth_tensors, masks,) def render_depth_multiview(self, camera_elevs, camera_azims): depth_maps = [] masks = [] for elev, azim in zip(camera_elevs, camera_azims): depth_map, mask = self.render.render_depth(elev, azim, return_type='th') depth_maps.append(depth_map) masks.append(mask) return depth_maps, masks class Hy3DDiffusersSchedulerConfig: @classmethod def INPUT_TYPES(s): return { "required": { "pipeline": ("HY3DDIFFUSERSPIPE",), "scheduler": (available_schedulers, { "default": 'Euler A' }), "sigmas": (["default", "karras", "exponential", "beta"],), }, } RETURN_TYPES = ("NOISESCHEDULER",) RETURN_NAMES = ("diffusers_scheduler",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, pipeline, scheduler, sigmas): scheduler_config = dict(pipeline.scheduler.config) if scheduler in scheduler_mapping: if scheduler == "DPM++SDE": scheduler_config["algorithm_type"] = "sde-dpmsolver++" else: scheduler_config.pop("algorithm_type", None) if sigmas == "default": scheduler_config["use_karras_sigmas"] = False scheduler_config["use_exponential_sigmas"] = False scheduler_config["use_beta_sigmas"] = False elif sigmas == "karras": scheduler_config["use_karras_sigmas"] = True scheduler_config["use_exponential_sigmas"] = False scheduler_config["use_beta_sigmas"] = False elif sigmas == "exponential": scheduler_config["use_karras_sigmas"] = False scheduler_config["use_exponential_sigmas"] = True scheduler_config["use_beta_sigmas"] = False elif sigmas == "beta": scheduler_config["use_karras_sigmas"] = False scheduler_config["use_exponential_sigmas"] = False scheduler_config["use_beta_sigmas"] = True noise_scheduler = scheduler_mapping[scheduler].from_config(scheduler_config) else: raise ValueError(f"Unknown scheduler: {scheduler}") return (noise_scheduler,) class Hy3DSampleMultiView: @classmethod def INPUT_TYPES(s): return { "required": { "pipeline": ("HY3DDIFFUSERSPIPE",), "ref_image": ("IMAGE", ), "normal_maps": ("IMAGE", ), "position_maps": ("IMAGE", ), "view_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16}), "steps": ("INT", {"default": 30, "min": 1}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "camera_config": ("HY3DCAMERA",), "scheduler": ("NOISESCHEDULER",), "denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "samples": ("LATENT", ), } } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, pipeline, ref_image, normal_maps, position_maps, view_size, seed, steps, camera_config=None, scheduler=None, denoise_strength=1.0, samples=None): device = mm.get_torch_device() mm.soft_empty_cache() torch.manual_seed(seed) generator=torch.Generator(device=pipeline.device).manual_seed(seed) input_image = ref_image.permute(0, 3, 1, 2).unsqueeze(0).to(device) device = mm.get_torch_device() if camera_config is None: selected_camera_azims = [0, 90, 180, 270, 0, 180] selected_camera_elevs = [0, 0, 0, 0, 90, -90] else: selected_camera_azims = camera_config["selected_camera_azims"] selected_camera_elevs = camera_config["selected_camera_elevs"] camera_info = [(((azim // 30) + 9) % 12) // {-90: 3, -45: 2, -20: 1, 0: 1, 20: 1, 45: 2, 90: 3}[ elev] + {-90: 36, -45: 30, -20: 0, 0: 12, 20: 24, 45: 30, 90: 40}[elev] for azim, elev in zip(selected_camera_azims, selected_camera_elevs)] #print(camera_info) normal_maps_np = (normal_maps * 255).to(torch.uint8).cpu().numpy() normal_maps_pil = [Image.fromarray(normal_map) for normal_map in normal_maps_np] position_maps_np = (position_maps * 255).to(torch.uint8).cpu().numpy() position_maps_pil = [Image.fromarray(position_map) for position_map in position_maps_np] control_images = normal_maps_pil + position_maps_pil for i in range(len(control_images)): control_images[i] = control_images[i].resize((view_size, view_size)) if control_images[i].mode == 'L': control_images[i] = control_images[i].point(lambda x: 255 if x > 1 else 0, mode='1') num_view = len(control_images) // 2 normal_image = [[control_images[i] for i in range(num_view)]] position_image = [[control_images[i + num_view] for i in range(num_view)]] callback = ComfyProgressCallback(total_steps=steps) if scheduler is not None: if not hasattr(self, "default_scheduler"): self.default_scheduler = pipeline.scheduler pipeline.scheduler = scheduler else: if hasattr(self, "default_scheduler"): pipeline.scheduler = self.default_scheduler multiview_images = pipeline( input_image, width=view_size, height=view_size, generator=generator, latents=samples["samples"] if samples is not None else None, num_in_batch = num_view, camera_info_gen = [camera_info], camera_info_ref = [[0]], normal_imgs = normal_image, position_imgs = position_image, num_inference_steps=steps, output_type="pt", callback_on_step_end=callback, callback_on_step_end_tensor_inputs=["latents", "prompt_embeds", "negative_prompt_embeds"], denoise_strength=denoise_strength ).images out_tensors = multiview_images.permute(0, 2, 3, 1).cpu().float() return (out_tensors,) class Hy3DBakeFromMultiview: @classmethod def INPUT_TYPES(s): return { "required": { "images": ("IMAGE", ), "renderer": ("MESHRENDER",), }, "optional": { "camera_config": ("HY3DCAMERA",), } } RETURN_TYPES = ("IMAGE", "MASK", "MESHRENDER") RETURN_NAMES = ("texture", "mask", "renderer") FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, images, renderer, camera_config=None): device = mm.get_torch_device() self.render = renderer multiviews = images.permute(0, 3, 1, 2) multiviews = multiviews.cpu().numpy() multiviews_pil = [Image.fromarray((image.transpose(1, 2, 0) * 255).astype(np.uint8)) for image in multiviews] if camera_config is None: selected_camera_azims = [0, 90, 180, 270, 0, 180] selected_camera_elevs = [0, 0, 0, 0, 90, -90] selected_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05] else: selected_camera_azims = camera_config["selected_camera_azims"] selected_camera_elevs = camera_config["selected_camera_elevs"] selected_view_weights = camera_config["selected_view_weights"] merge_method = 'fast' self.bake_exp = 4 texture, mask = self.bake_from_multiview(multiviews_pil, selected_camera_elevs, selected_camera_azims, selected_view_weights, method=merge_method) mask = mask.squeeze(-1).cpu().float() texture = texture.unsqueeze(0).cpu().float() return (texture, mask, self.render) def bake_from_multiview(self, views, camera_elevs, camera_azims, view_weights, method='graphcut'): project_textures, project_weighted_cos_maps = [], [] project_boundary_maps = [] pbar = ProgressBar(len(views)) 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.bake_exp) project_textures.append(project_texture) project_weighted_cos_maps.append(project_cos_map) project_boundary_maps.append(project_boundary_map) pbar.update(1) 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 class Hy3DMeshVerticeInpaintTexture: @classmethod def INPUT_TYPES(s): return { "required": { "texture": ("IMAGE", ), "mask": ("MASK", ), "renderer": ("MESHRENDER",), }, } RETURN_TYPES = ("IMAGE", "MASK", "MESHRENDER" ) RETURN_NAMES = ("texture", "mask", "renderer" ) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, texture, renderer, mask): from .hy3dgen.texgen.differentiable_renderer.mesh_processor import meshVerticeInpaint vtx_pos, pos_idx, vtx_uv, uv_idx = renderer.get_mesh() mask_np = (mask.squeeze(-1).squeeze(0).cpu().numpy() * 255).astype(np.uint8) texture_np = texture.squeeze(0).cpu().numpy() * 255 texture_np, mask_np = meshVerticeInpaint( texture_np, mask_np, vtx_pos, vtx_uv, pos_idx, uv_idx) texture_tensor = torch.from_numpy(texture_np).float() / 255.0 texture_tensor = texture_tensor.unsqueeze(0) mask_tensor = torch.from_numpy(mask_np).float() / 255.0 mask_tensor = mask_tensor.unsqueeze(0) return (texture_tensor, mask_tensor, renderer) class CV2InpaintTexture: @classmethod def INPUT_TYPES(s): return { "required": { "texture": ("IMAGE", ), "mask": ("MASK", ), "inpaint_radius": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}), "inpaint_method": (["ns", "telea"], {"default": "ns"}), }, } RETURN_TYPES = ("IMAGE", ) RETURN_NAMES = ("texture", ) FUNCTION = "inpaint" CATEGORY = "Hunyuan3DWrapper" def inpaint(self, texture, mask, inpaint_radius, inpaint_method): import cv2 mask = 1 - mask mask_np = (mask.squeeze(-1).squeeze(0).cpu().numpy() * 255).astype(np.uint8) texture_np = (texture.squeeze(0).cpu().numpy() * 255).astype(np.uint8) if inpaint_method == "ns": inpaint_algo = cv2.INPAINT_NS elif inpaint_method == "telea": inpaint_algo = cv2.INPAINT_TELEA texture_np = cv2.inpaint( texture_np, mask_np, inpaint_radius, inpaint_algo) texture_tensor = torch.from_numpy(texture_np).float() / 255.0 texture_tensor = texture_tensor.unsqueeze(0) return (texture_tensor, ) class Hy3DApplyTexture: @classmethod def INPUT_TYPES(s): return { "required": { "texture": ("IMAGE", ), "renderer": ("MESHRENDER",), }, } RETURN_TYPES = ("HY3DMESH", ) RETURN_NAMES = ("mesh", ) FUNCTION = "apply" CATEGORY = "Hunyuan3DWrapper" def apply(self, texture, renderer): self.render = renderer self.render.set_texture(texture.squeeze(0)) textured_mesh = self.render.save_mesh() return (textured_mesh,) #region Mesh class Hy3DLoadMesh: @classmethod def INPUT_TYPES(s): return { "required": { "glb_path": ("STRING", {"default": "", "tooltip": "The glb path with mesh to load."}), } } RETURN_TYPES = ("HY3DMESH",) RETURN_NAMES = ("mesh",) OUTPUT_TOOLTIPS = ("The glb model with mesh to texturize.",) FUNCTION = "load" CATEGORY = "Hunyuan3DWrapper" DESCRIPTION = "Loads a glb model from the given path." def load(self, glb_path): mesh = trimesh.load(glb_path, force="mesh") return (mesh,) class Hy3DUploadMesh: @classmethod def INPUT_TYPES(s): mesh_extensions = ['glb', 'gltf', 'obj', 'ply', 'stl', '3mf'] input_dir = folder_paths.get_input_directory() files = [] for f in os.listdir(input_dir): if os.path.isfile(os.path.join(input_dir, f)): file_parts = f.split('.') if len(file_parts) > 1 and (file_parts[-1] in mesh_extensions): files.append(f) return { "required": { "mesh": (sorted(files),), } } RETURN_TYPES = ("HY3DMESH",) RETURN_NAMES = ("mesh",) OUTPUT_TOOLTIPS = ("The glb model with mesh to texturize.",) FUNCTION = "load" CATEGORY = "Hunyuan3DWrapper" DESCRIPTION = "Loads a glb model from the given path." def load(self, mesh): path = mesh.strip() if path.startswith("\""): path = path[1:] if path.endswith("\""): path = path[:-1] mesh_file = folder_paths.get_annotated_filepath(path) loaded_mesh = trimesh.load(mesh_file, force="mesh") return (loaded_mesh,) class Hy3DGenerateMesh: @classmethod def INPUT_TYPES(s): return { "required": { "pipeline": ("HY3DMODEL",), "image": ("IMAGE", ), "guidance_scale": ("FLOAT", {"default": 5.5, "min": 0.0, "max": 100.0, "step": 0.01}), "steps": ("INT", {"default": 30, "min": 1}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "mask": ("MASK", ), } } RETURN_TYPES = ("HY3DLATENT",) RETURN_NAMES = ("latents",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, pipeline, image, steps, guidance_scale, seed, mask=None): mm.unload_all_models() mm.soft_empty_cache() device = mm.get_torch_device() offload_device = mm.unet_offload_device() image = image.permute(0, 3, 1, 2).to(device) image = image * 2 - 1 if mask is not None: mask = mask.unsqueeze(0).to(device) if mask.shape[2] != image.shape[2] or mask.shape[3] != image.shape[3]: mask = F.interpolate(mask, size=(image.shape[2], image.shape[3]), mode='nearest') pipeline.to(device) try: torch.cuda.reset_peak_memory_stats(device) except: pass latents = pipeline( image=image, mask=mask, num_inference_steps=steps, guidance_scale=guidance_scale, generator=torch.manual_seed(seed)) print_memory(device) try: torch.cuda.reset_peak_memory_stats(device) except: pass pipeline.to(offload_device) return (latents, ) class Hy3DVAEDecode: @classmethod def INPUT_TYPES(s): return { "required": { "vae": ("HY3DVAE",), "latents": ("HY3DLATENT", ), "box_v": ("FLOAT", {"default": 1.01, "min": -10.0, "max": 10.0, "step": 0.001}), "octree_resolution": ("INT", {"default": 384, "min": 64, "max": 4096, "step": 16}), "num_chunks": ("INT", {"default": 8000, "min": 1, "max": 10000000, "step": 1, "tooltip": "Number of chunks to process at once, higher values use more memory, but make the process faster"}), "mc_level": ("FLOAT", {"default": 0, "min": -1.0, "max": 1.0, "step": 0.0001}), "mc_algo": (["mc", "dmc"], {"default": "mc"}), }, } RETURN_TYPES = ("HY3DMESH",) RETURN_NAMES = ("mesh",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, vae, latents, box_v, octree_resolution, mc_level, num_chunks, mc_algo): device = mm.get_torch_device() offload_device = mm.unet_offload_device() vae.to(device) latents = 1. / vae.scale_factor * latents latents = vae(latents) outputs = vae.latents2mesh( latents, bounds=box_v, mc_level=mc_level, num_chunks=num_chunks, octree_resolution=octree_resolution, mc_algo=mc_algo, )[0] vae.to(offload_device) outputs.mesh_f = outputs.mesh_f[:, ::-1] mesh_output = trimesh.Trimesh(outputs.mesh_v, outputs.mesh_f) log.info(f"Decoded mesh with {mesh_output.vertices.shape[0]} vertices and {mesh_output.faces.shape[0]} faces") return (mesh_output, ) class Hy3DPostprocessMesh: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "remove_floaters": ("BOOLEAN", {"default": True}), "remove_degenerate_faces": ("BOOLEAN", {"default": True}), "reduce_faces": ("BOOLEAN", {"default": True}), "max_facenum": ("INT", {"default": 40000, "min": 1, "max": 10000000, "step": 1}), "smooth_normals": ("BOOLEAN", {"default": False}), }, } RETURN_TYPES = ("HY3DMESH",) RETURN_NAMES = ("mesh",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh, remove_floaters, remove_degenerate_faces, reduce_faces, max_facenum, smooth_normals): new_mesh = mesh.copy() if remove_floaters: new_mesh = FloaterRemover()(new_mesh) log.info(f"Removed floaters, resulting in {new_mesh.vertices.shape[0]} vertices and {new_mesh.faces.shape[0]} faces") if remove_degenerate_faces: new_mesh = DegenerateFaceRemover()(new_mesh) log.info(f"Removed degenerate faces, resulting in {new_mesh.vertices.shape[0]} vertices and {new_mesh.faces.shape[0]} faces") if reduce_faces: new_mesh = FaceReducer()(new_mesh, max_facenum=max_facenum) log.info(f"Reduced faces, resulting in {new_mesh.vertices.shape[0]} vertices and {new_mesh.faces.shape[0]} faces") if smooth_normals: new_mesh.vertex_normals = trimesh.smoothing.get_vertices_normals(new_mesh) return (new_mesh, ) class Hy3DFastSimplifyMesh: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "target_count": ("INT", {"default": 40000, "min": 1, "max": 100000000, "step": 1, "tooltip": "Target number of triangles"}), "aggressiveness": ("INT", {"default": 7, "min": 0, "max": 100, "step": 1, "tooltip": "Parameter controlling the growth rate of the threshold at each iteration when lossless is False."}), "max_iterations": ("INT", {"default": 100, "min": 1, "max": 1000, "step": 1, "tooltip": "Maximal number of iterations"}), "update_rate": ("INT", {"default": 5, "min": 1, "max": 1000, "step": 1, "tooltip": "Number of iterations between each update"}), "preserve_border": ("BOOLEAN", {"default": True, "tooltip": "Flag for preserving the vertices situated on open borders."}), "lossless": ("BOOLEAN", {"default": False, "tooltip": "Flag for using the lossless simplification method. Sets the update rate to 1"}), "threshold_lossless": ("FLOAT", {"default": 1e-3, "min": 0.0, "max": 1.0, "step": 0.0001, "tooltip": "Threshold for the lossless simplification method."}), }, } RETURN_TYPES = ("HY3DMESH",) RETURN_NAMES = ("mesh",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" DESCRIPTION = "Simplifies the mesh using Fast Quadric Mesh Reduction: https://github.com/Kramer84/pyfqmr-Fast-Quadric-Mesh-Reduction" def process(self, mesh, target_count, aggressiveness, preserve_border, max_iterations,lossless, threshold_lossless, update_rate): new_mesh = mesh.copy() try: import pyfqmr except ImportError: raise ImportError("pyfqmr not found. Please install it using 'pip install pyfqmr' https://github.com/Kramer84/pyfqmr-Fast-Quadric-Mesh-Reduction") mesh_simplifier = pyfqmr.Simplify() mesh_simplifier.setMesh(mesh.vertices, mesh.faces) mesh_simplifier.simplify_mesh( target_count=target_count, aggressiveness=aggressiveness, update_rate=update_rate, max_iterations=max_iterations, preserve_border=preserve_border, verbose=True, lossless=lossless, threshold_lossless=threshold_lossless ) new_mesh.vertices, new_mesh.faces, _ = mesh_simplifier.getMesh() log.info(f"Simplified mesh to {target_count} vertices, resulting in {new_mesh.vertices.shape[0]} vertices and {new_mesh.faces.shape[0]} faces") return (new_mesh, ) class Hy3DMeshInfo: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), }, } RETURN_TYPES = ("HY3DMESH", "INT", "INT", ) RETURN_NAMES = ("mesh", "vertices", "faces",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh): vertices_count = mesh.vertices.shape[0] faces_count = mesh.faces.shape[0] log.info(f"Hy3DMeshInfo: Mesh has {vertices_count} vertices and {mesh.faces.shape[0]} faces") return {"ui": { "text": [f"{vertices_count:,.0f}x{faces_count:,.0f}"]}, "result": (mesh, vertices_count, faces_count) } class Hy3DIMRemesh: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "merge_vertices": ("BOOLEAN", {"default": True}), "vertex_count": ("INT", {"default": 10000, "min": 100, "max": 10000000, "step": 1}), "smooth_iter": ("INT", {"default": 8, "min": 0, "max": 100, "step": 1}), "align_to_boundaries": ("BOOLEAN", {"default": True}), "triangulate_result": ("BOOLEAN", {"default": True}), }, } RETURN_TYPES = ("HY3DMESH",) RETURN_NAMES = ("mesh",) FUNCTION = "remesh" CATEGORY = "Hunyuan3DWrapper" DESCRIPTION = "Remeshes the mesh using instant-meshes: https://github.com/wjakob/instant-meshes, Note: this will remove all vertex colors and textures." def remesh(self, mesh, merge_vertices, vertex_count, smooth_iter, align_to_boundaries, triangulate_result): try: import pynanoinstantmeshes as PyNIM except ImportError: raise ImportError("pynanoinstantmeshes not found. Please install it using 'pip install pynanoinstantmeshes'") new_mesh = mesh.copy() if merge_vertices: mesh.merge_vertices(new_mesh) new_verts, new_faces = PyNIM.remesh( np.array(mesh.vertices, dtype=np.float32), np.array(mesh.faces, dtype=np.uint32), vertex_count, align_to_boundaries=align_to_boundaries, smooth_iter=smooth_iter ) if new_verts.shape[0] - 1 != new_faces.max(): # Skip test as the meshing failed raise ValueError("Instant-meshes failed to remesh the mesh") new_verts = new_verts.astype(np.float32) if triangulate_result: new_faces = trimesh.geometry.triangulate_quads(new_faces) new_mesh = trimesh.Trimesh(new_verts, new_faces) return (new_mesh, ) class Hy3DGetMeshPBRTextures: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "texture" : (["base_color", "emissive", "metallic_roughness", "normal", "occlusion"], ), }, } RETURN_TYPES = ("IMAGE", ) RETURN_NAMES = ("image",) FUNCTION = "get_textures" CATEGORY = "Hunyuan3DWrapper" def get_textures(self, mesh, texture): TEXTURE_MAPPING = { 'base_color': ('baseColorTexture', "Base color"), 'emissive': ('emissiveTexture', "Emissive"), 'metallic_roughness': ('metallicRoughnessTexture', "Metallic roughness"), 'normal': ('normalTexture', "Normal"), 'occlusion': ('occlusionTexture', "Occlusion"), } texture_attr, texture_name = TEXTURE_MAPPING[texture] texture_data = getattr(mesh.visual.material, texture_attr) if texture_data is None: raise ValueError(f"{texture_name} texture not found") to_tensor = transforms.ToTensor() return (to_tensor(texture_data).unsqueeze(0).permute(0, 2, 3, 1).cpu().float(),) class Hy3DSetMeshPBRTextures: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "image": ("IMAGE", ), "texture" : (["base_color", "emissive", "metallic_roughness", "normal", "occlusion"], ), }, } RETURN_TYPES = ("HY3DMESH", ) RETURN_NAMES = ("mesh",) FUNCTION = "set_textures" CATEGORY = "Hunyuan3DWrapper" def set_textures(self, mesh, image, texture): from trimesh.visual.material import SimpleMaterial if isinstance(mesh.visual.material, SimpleMaterial): log.info("Found SimpleMaterial, Converting to PBRMaterial") mesh.visual.material = mesh.visual.material.to_pbr() TEXTURE_MAPPING = { 'base_color': ('baseColorTexture', "Base color"), 'emissive': ('emissiveTexture', "Emissive"), 'metallic_roughness': ('metallicRoughnessTexture', "Metallic roughness"), 'normal': ('normalTexture', "Normal"), 'occlusion': ('occlusionTexture', "Occlusion"), } new_mesh = mesh.copy() texture_attr, texture_name = TEXTURE_MAPPING[texture] image_np = (image[0].cpu().numpy() * 255).astype(np.uint8) if image_np.shape[2] == 4: # RGBA pil_image = Image.fromarray(image_np, 'RGBA') else: # RGB pil_image = Image.fromarray(image_np, 'RGB') setattr(new_mesh.visual.material, texture_attr, pil_image) return (new_mesh,) class Hy3DSetMeshPBRAttributes: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "baseColorFactor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "emissiveFactor": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), "metallicFactor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "roughnessFactor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "doubleSided": ("BOOLEAN", {"default": False}), }, } RETURN_TYPES = ("HY3DMESH", ) RETURN_NAMES = ("mesh",) FUNCTION = "set_textures" CATEGORY = "Hunyuan3DWrapper" def set_textures(self, mesh, baseColorFactor, emissiveFactor, metallicFactor, roughnessFactor, doubleSided): new_mesh = mesh.copy() new_mesh.visual.material.baseColorFactor = [baseColorFactor, baseColorFactor, baseColorFactor, 1.0] new_mesh.visual.material.emissiveFactor = [emissiveFactor, emissiveFactor, emissiveFactor] new_mesh.visual.material.metallicFactor = metallicFactor new_mesh.visual.material.roughnessFactor = roughnessFactor new_mesh.visual.material.doubleSided = doubleSided return (new_mesh,) class Hy3DExportMesh: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "filename_prefix": ("STRING", {"default": "3D/Hy3D"}), "file_format": (["glb", "obj", "ply", "stl", "3mf", "dae"],), }, } RETURN_TYPES = ("STRING",) RETURN_NAMES = ("glb_path",) FUNCTION = "process" CATEGORY = "Hunyuan3DWrapper" def process(self, mesh, filename_prefix, file_format): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory()) output_glb_path = Path(full_output_folder, f'{filename}_{counter:05}_.{file_format}') output_glb_path.parent.mkdir(exist_ok=True) mesh.export(output_glb_path, file_type=file_format) relative_path = Path(subfolder) / f'{filename}_{counter:05}_.{file_format}' return (str(relative_path), ) class Hy3DNvdiffrastRenderer: @classmethod def INPUT_TYPES(s): return { "required": { "mesh": ("HY3DMESH",), "render_type": (["textured", "vertex_colors", "normals","depth",],), "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16, "tooltip": "Width of the rendered image"}), "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16, "tooltip": "Height of the rendered image"}), "ssaa": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1, "tooltip": "Super-sampling anti-aliasing"}), "num_frames": ("INT", {"default": 30, "min": 1, "max": 1000, "step": 1, "tooltip": "Number of frames to render"}), "camera_distance": ("FLOAT", {"default": 2.0, "min": -100.1, "max": 1000.0, "step": 0.01, "tooltip": "Camera distance from the object"}), "yaw": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.01, "tooltip": "Start yaw in radians"}), "pitch": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.01, "tooltip": "Start pitch in radians"}), "fov": ("FLOAT", {"default": 60.0, "min": 1.0, "max": 179.0, "step": 0.01, "tooltip": "Camera field of view in degrees"}), "near": ("FLOAT", {"default": 0.1, "min": 0.001, "max": 1000.0, "step": 0.01, "tooltip": "Camera near clipping plane"}), "far": ("FLOAT", {"default": 1000.0, "min": 1.0, "max": 10000.0, "step": 0.01, "tooltip": "Camera far clipping plane"}), }, } RETURN_TYPES = ("IMAGE", "MASK",) RETURN_NAMES = ("image", "mask") FUNCTION = "render" CATEGORY = "Hunyuan3DWrapper" def render(self, mesh, width, height, camera_distance, yaw, pitch, fov, near, far, num_frames, ssaa, render_type): try: import nvdiffrast.torch as dr except ImportError: raise ImportError("nvdiffrast not found. Please install it https://github.com/NVlabs/nvdiffrast") try: from .utils import rotate_mesh_matrix, yaw_pitch_r_fov_to_extrinsics_intrinsics, intrinsics_to_projection except ImportError: raise ImportError("utils3d not found. Please install it 'pip install git+https://github.com/EasternJournalist/utils3d.git#egg=utils3d'") # Create GL context device = mm.get_torch_device() glctx = dr.RasterizeCudaContext() mesh_copy = mesh.copy() mesh_copy = rotate_mesh_matrix(mesh_copy, 90, 'x') mesh_copy = rotate_mesh_matrix(mesh_copy, 180, 'z') width, height = width * ssaa, height * ssaa # Get UV coordinates and texture if available if hasattr(mesh_copy.visual, 'uv') and hasattr(mesh_copy.visual, 'material'): uvs = torch.tensor(mesh_copy.visual.uv, dtype=torch.float32, device=device).contiguous() # Get texture from material if hasattr(mesh_copy.visual.material, 'baseColorTexture'): pil_texture = getattr(mesh_copy.visual.material, "baseColorTexture") elif hasattr(mesh_copy.visual.material, 'image'): pil_texture = getattr(mesh_copy.visual.material, "image") pil_texture = pil_texture.transpose(Image.FLIP_TOP_BOTTOM) # Convert PIL to tensor [B,C,H,W] transform = transforms.Compose([ transforms.ToTensor(), ]) texture = transform(pil_texture).to(device) texture = texture.unsqueeze(0).permute(0, 2, 3, 1).contiguous() #need to be contiguous for nvdiffrast else: log.warning("No texture found") # Fallback to vertex colors if no texture uvs = None texture = None # Get vertices and faces from trimesh vertices = torch.tensor(mesh_copy.vertices, dtype=torch.float32, device=device).unsqueeze(0) faces = torch.tensor(mesh_copy.faces, dtype=torch.int32, device=device) yaws = torch.linspace(yaw, yaw + torch.pi * 2, num_frames) pitches = [pitch] * num_frames yaws = yaws.tolist() r = camera_distance extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(yaws, pitches, r, fov) image_list = [] mask_list = [] pbar = ProgressBar(num_frames) for j, (extr, intr) in tqdm(enumerate(zip(extrinsics, intrinsics)), desc='Rendering', disable=False): perspective = intrinsics_to_projection(intr, near, far) RT = extr.unsqueeze(0) full_proj = (perspective @ extr).unsqueeze(0) # Transform vertices to clip space vertices_homo = torch.cat([vertices, torch.ones_like(vertices[..., :1])], dim=-1) vertices_camera = torch.bmm(vertices_homo, RT.transpose(-1, -2)) vertices_clip = torch.bmm(vertices_homo, full_proj.transpose(-1, -2)) # Rasterize with proper shape [batch=1, num_vertices, 4] rast_out, _ = dr.rasterize(glctx, vertices_clip, faces, (height, width)) if render_type == "textured": if uvs is not None and texture is not None: # Interpolate UV coordinates uv_attr, _= dr.interpolate(uvs.unsqueeze(0), rast_out, faces) # Sample texture using interpolated UVs image = dr.texture(tex=texture, uv=uv_attr) image = dr.antialias(image, rast_out, vertices_clip, faces) else: raise Exception("No texture found") elif render_type == "vertex_colors": # Fallback to vertex color rendering vertex_colors = (vertices - vertices.min()) / (vertices.max() - vertices.min()) image = dr.interpolate(vertex_colors, rast_out, faces)[0] elif render_type == "depth": depth_values = vertices_camera[..., 2:3].contiguous() depth_values = (depth_values - depth_values.min()) / (depth_values.max() - depth_values.min()) depth_values = 1 - depth_values image = dr.interpolate(depth_values, rast_out, faces)[0] image = dr.antialias(image, rast_out, vertices_clip, faces) elif "normals" in render_type: normals_tensor = torch.tensor(mesh_copy.vertex_normals, dtype=torch.float32, device=device).contiguous() faces_tensor = torch.tensor(mesh_copy.faces, dtype=torch.int32, device=device).contiguous() normal_image_tensors = dr.interpolate(normals_tensor, rast_out, faces_tensor)[0] normal_image_tensors = dr.antialias(normal_image_tensors, rast_out, vertices_clip, faces) normal_image_tensors = torch.nn.functional.normalize(normal_image_tensors, dim=-1) image = (normal_image_tensors + 1) * 0.5 # Create background color background_color = torch.zeros((1, height, width, 3), device=device) # Get alpha mask from rasterization mask = rast_out[..., -1:] mask = (mask > 0).float() # Blend rendered image with background image = image * mask + background_color * (1 - mask) image_list.append(image) mask_list.append(mask) pbar.update(1) import torch.nn.functional as F image_out = torch.cat(image_list, dim=0) if ssaa > 1: image_out = F.interpolate(image_out.permute(0, 3, 1, 2), (width, height), mode='bilinear', align_corners=False, antialias=True) image_out = image_out.permute(0, 2, 3, 1) mask_out = torch.cat(mask_list, dim=0).squeeze(-1) return (image_out.cpu().float(), mask_out.cpu().float(),) NODE_CLASS_MAPPINGS = { "Hy3DModelLoader": Hy3DModelLoader, "Hy3DGenerateMesh": Hy3DGenerateMesh, "Hy3DExportMesh": Hy3DExportMesh, "DownloadAndLoadHy3DDelightModel": DownloadAndLoadHy3DDelightModel, "DownloadAndLoadHy3DPaintModel": DownloadAndLoadHy3DPaintModel, "Hy3DDelightImage": Hy3DDelightImage, "Hy3DRenderMultiView": Hy3DRenderMultiView, "Hy3DBakeFromMultiview": Hy3DBakeFromMultiview, "Hy3DTorchCompileSettings": Hy3DTorchCompileSettings, "Hy3DPostprocessMesh": Hy3DPostprocessMesh, "Hy3DLoadMesh": Hy3DLoadMesh, "Hy3DUploadMesh": Hy3DUploadMesh, "Hy3DCameraConfig": Hy3DCameraConfig, "Hy3DMeshUVWrap": Hy3DMeshUVWrap, "Hy3DSampleMultiView": Hy3DSampleMultiView, "Hy3DMeshVerticeInpaintTexture": Hy3DMeshVerticeInpaintTexture, "Hy3DApplyTexture": Hy3DApplyTexture, "CV2InpaintTexture": CV2InpaintTexture, "Hy3DRenderMultiViewDepth": Hy3DRenderMultiViewDepth, "Hy3DGetMeshPBRTextures": Hy3DGetMeshPBRTextures, "Hy3DSetMeshPBRTextures": Hy3DSetMeshPBRTextures, "Hy3DSetMeshPBRAttributes": Hy3DSetMeshPBRAttributes, "Hy3DVAEDecode": Hy3DVAEDecode, "Hy3DRenderSingleView": Hy3DRenderSingleView, "Hy3DDiffusersSchedulerConfig": Hy3DDiffusersSchedulerConfig, "Hy3DIMRemesh": Hy3DIMRemesh, "Hy3DMeshInfo": Hy3DMeshInfo, "Hy3DFastSimplifyMesh": Hy3DFastSimplifyMesh, "Hy3DNvdiffrastRenderer": Hy3DNvdiffrastRenderer } NODE_DISPLAY_NAME_MAPPINGS = { "Hy3DModelLoader": "Hy3DModelLoader", "Hy3DGenerateMesh": "Hy3DGenerateMesh", "Hy3DExportMesh": "Hy3DExportMesh", "DownloadAndLoadHy3DDelightModel": "(Down)Load Hy3D DelightModel", "DownloadAndLoadHy3DPaintModel": "(Down)Load Hy3D PaintModel", "Hy3DDelightImage": "Hy3DDelightImage", "Hy3DRenderMultiView": "Hy3D Render MultiView", "Hy3DBakeFromMultiview": "Hy3D Bake From Multiview", "Hy3DTorchCompileSettings": "Hy3D Torch Compile Settings", "Hy3DPostprocessMesh": "Hy3D Postprocess Mesh", "Hy3DLoadMesh": "Hy3D Load Mesh", "Hy3DUploadMesh": "Hy3D Upload Mesh", "Hy3DCameraConfig": "Hy3D Camera Config", "Hy3DMeshUVWrap": "Hy3D Mesh UV Wrap", "Hy3DSampleMultiView": "Hy3D Sample MultiView", "Hy3DMeshVerticeInpaintTexture": "Hy3D Mesh Vertice Inpaint Texture", "Hy3DApplyTexture": "Hy3D Apply Texture", "CV2InpaintTexture": "CV2 Inpaint Texture", "Hy3DRenderMultiViewDepth": "Hy3D Render MultiView Depth", "Hy3DGetMeshPBRTextures": "Hy3D Get Mesh PBR Textures", "Hy3DSetMeshPBRTextures": "Hy3D Set Mesh PBR Textures", "Hy3DSetMeshPBRAttributes": "Hy3D Set Mesh PBR Attributes", "Hy3DVAEDecode": "Hy3D VAE Decode", "Hy3DRenderSingleView": "Hy3D Render SingleView", "Hy3DDiffusersSchedulerConfig": "Hy3D Diffusers Scheduler Config", "Hy3DIMRemesh": "Hy3D Instant-Meshes Remesh", "Hy3DMeshInfo": "Hy3D Mesh Info", "Hy3DFastSimplifyMesh": "Hy3D Fast Simplify Mesh", "Hy3DNvdiffrastRenderer": "Hy3D Nvdiffrast Renderer" }