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ComfyUI-Hunyuan3DWrapper/nodes.py
kijai fb7549668c refactor
2025-01-23 16:41:39 +02:00

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import os
import torch
from PIL import Image
from pathlib import Path
import numpy as np
from .hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline, FaceReducer, FloaterRemover, DegenerateFaceRemover
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, pil_list_to_torch_batch
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"}),
}
}
RETURN_TYPES = ("HY3DMODEL",)
RETURN_NAMES = ("pipeline", )
FUNCTION = "loadmodel"
CATEGORY = "Hunyuan3DWrapper"
def loadmodel(self, model, compile_args=None):
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 = 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)
return (pipe,)
class DownloadAndLoadHy3DDelightModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (["hunyuan3d-delight-v2-0"],),
},
}
RETURN_TYPES = ("DELIGHTMODEL",)
RETURN_NAMES = ("delight_pipe", )
FUNCTION = "loadmodel"
CATEGORY = "Hunyuan3DWrapper"
def loadmodel(self, model):
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=["*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)
delight_pipe.enable_model_cpu_offload()
return (delight_pipe,)
class Hy3DDelightImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"delight_pipe": ("DELIGHTMODEL",),
"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": 1.5, "min": 0.0, "max": 100.0, "step": 0.01}),
"cfg_text": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
"seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "process"
CATEGORY = "Hunyuan3DWrapper"
def process(self, delight_pipe, image, width, height, cfg_image, cfg_text, steps, seed):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
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=cfg_text,
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"],),
},
}
RETURN_TYPES = ("HY3DPAINTMODEL",)
RETURN_NAMES = ("multiview_pipe", )
FUNCTION = "loadmodel"
CATEGORY = "Hunyuan3DWrapper"
def loadmodel(self, model):
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}*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
custom_pipeline_path = os.path.join(script_directory, 'hy3dgen', 'texgen', 'hunyuanpaint')
pipeline = DiffusionPipeline.from_pretrained(
model_path,
custom_pipeline=custom_pipeline_path,
torch_dtype=torch.float16)
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing='trailing')
pipeline.enable_model_cpu_offload()
return (pipeline,)
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}),
},
}
RETURN_TYPES = ("HY3DCAMERA",)
RETURN_NAMES = ("camera_config",)
FUNCTION = "process"
CATEGORY = "Hunyuan3DWrapper"
def process(self, camera_azimuths, camera_elevations, view_weights):
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
}
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",),
}
}
RETURN_TYPES = ("IMAGE", "IMAGE", "MESHRENDER")
RETURN_NAMES = ("normal_maps", "position_maps", "renderer")
FUNCTION = "process"
CATEGORY = "Hunyuan3DWrapper"
def process(self, mesh, render_size, texture_size, camera_config=None):
from .hy3dgen.texgen.differentiable_renderer.mesh_render import MeshRender
self.render = MeshRender(
default_resolution=render_size,
texture_size=texture_size)
self.render.load_mesh(mesh)
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"]
normal_maps = self.render_normal_multiview(
selected_camera_elevs, selected_camera_azims, use_abs_coor=True)
normal_tensors = torch.stack(normal_maps, dim=0)
position_maps = self.render_position_multiview(
selected_camera_elevs, selected_camera_azims)
position_tensors = torch.stack(position_maps, dim=0)
return (normal_tensors, position_tensors, self.render,)
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='th')
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='th')
position_maps.append(position_map)
return position_maps
class Hy3DSampleMultiView:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("HY3DPAINTMODEL",),
"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",),
}
}
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):
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) // {-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(selected_camera_azims, selected_camera_elevs)]
normal_maps_np = (normal_maps * 255).clamp(0, 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).clamp(0, 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)
multiview_images = pipeline(
input_image,
width=view_size,
height=view_size,
generator=generator,
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"]
).images
out_tensors = multiview_images.permute(0, 2, 3, 1).cpu().float()
return (out_tensors,)
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
class Hy3DBakeFromMultiview:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE", ),
"renderer": ("MESHRENDER",),
},
}
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 Hy3DInpaintTexture:
@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
import cv2
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,)
class Hy3DGenerateMesh:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("HY3DMODEL",),
"image": ("IMAGE", ),
"octree_resolution": ("INT", {"default": 256, "min": 64, "max": 4096, "step": 16}),
"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 = ("HY3DMESH",)
RETURN_NAMES = ("mesh",)
FUNCTION = "process"
CATEGORY = "Hunyuan3DWrapper"
def process(self, pipeline, image, steps, guidance_scale, octree_resolution, seed, mask=None):
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)
pipeline.to(device)
try:
torch.cuda.reset_peak_memory_stats(device)
except:
pass
mesh = pipeline(
image=image,
mask=mask,
num_inference_steps=steps,
mc_algo='mc',
guidance_scale=guidance_scale,
octree_resolution=octree_resolution,
generator=torch.manual_seed(seed))[0]
log.info(f"Generated mesh with {mesh.vertices.shape[0]} vertices and {mesh.faces.shape[0]} faces")
print_memory(device)
try:
torch.cuda.reset_peak_memory_stats(device)
except:
pass
pipeline.to(offload_device)
return (mesh, )
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}),
},
}
RETURN_TYPES = ("HY3DMESH",)
RETURN_NAMES = ("mesh",)
FUNCTION = "process"
CATEGORY = "Hunyuan3DWrapper"
def process(self, mesh, remove_floaters, remove_degenerate_faces, reduce_faces, max_facenum):
if remove_floaters:
mesh = FloaterRemover()(mesh)
log.info(f"Removed floaters, resulting in {mesh.vertices.shape[0]} vertices and {mesh.faces.shape[0]} faces")
if remove_degenerate_faces:
mesh = DegenerateFaceRemover()(mesh)
log.info(f"Removed degenerate faces, resulting in {mesh.vertices.shape[0]} vertices and {mesh.faces.shape[0]} faces")
if reduce_faces:
mesh = FaceReducer()(mesh, max_facenum=max_facenum)
log.info(f"Reduced faces, resulting in {mesh.vertices.shape[0]} vertices and {mesh.faces.shape[0]} faces")
return (mesh, )
class Hy3DExportMesh:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mesh": ("HY3DMESH",),
"filename_prefix": ("STRING", {"default": "3D/Hy3D"}),
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("glb_path",)
FUNCTION = "process"
CATEGORY = "Hunyuan3DWrapper"
def process(self, mesh, filename_prefix):
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}_.glb')
output_glb_path.parent.mkdir(exist_ok=True)
mesh.export(output_glb_path)
relative_path = Path(subfolder) / f'{filename}_{counter:05}_.glb'
return (str(relative_path), )
NODE_CLASS_MAPPINGS = {
"Hy3DModelLoader": Hy3DModelLoader,
"Hy3DGenerateMesh": Hy3DGenerateMesh,
"Hy3DExportMesh": Hy3DExportMesh,
"DownloadAndLoadHy3DDelightModel": DownloadAndLoadHy3DDelightModel,
"DownloadAndLoadHy3DPaintModel": DownloadAndLoadHy3DPaintModel,
"Hy3DDelightImage": Hy3DDelightImage,
"Hy3DRenderMultiView": Hy3DRenderMultiView,
"Hy3DBakeFromMultiview": Hy3DBakeFromMultiview,
"Hy3DTorchCompileSettings": Hy3DTorchCompileSettings,
"Hy3DPostprocessMesh": Hy3DPostprocessMesh,
"Hy3DCameraConfig": Hy3DCameraConfig,
"Hy3DMeshUVWrap": Hy3DMeshUVWrap,
"Hy3DSampleMultiView": Hy3DSampleMultiView,
"Hy3DInpaintTexture": Hy3DInpaintTexture,
"Hy3DApplyTexture": Hy3DApplyTexture,
"CV2InpaintTexture": CV2InpaintTexture,
}
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",
"Hy3DCameraConfig": "Hy3D Camera Config",
"Hy3DMeshUVWrap": "Hy3D Mesh UV Wrap",
"Hy3DSampleMultiView": "Hy3D Sample MultiView",
"Hy3DInpaintTexture": "Hy3D Inpaint Texture",
"Hy3DApplyTexture": "Hy3D Apply Texture",
"CV2InpaintTexture": "CV2 Inpaint Texture",
}
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