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Hunyuan3D_2.1_Low_VRAM/gradio_app.py
Akasei 6534f4ba15 fix: adaptive VRAM strategy + force rembg CPU to prevent OOM 
Two root causes of CUDA OOM fixed:

1. onnxruntime-gpu CUDAExecutionProvider pre-allocated ~12GB VRAM arena
   for bria-rmbg background removal, starving PyTorch models.
   Fix: force CPUExecutionProvider in BackgroundRemover (rembg is
   lightweight, runs fine on CPU, frees all VRAM for shape/tex).

2. Previous 'always delete' strategy was wasteful on high-RAM machines.
   New adaptive strategy checks available system RAM at runtime:
   - RAM >= 16GB free: offload i23d to CPU (.to('cpu')) — fast, ~1s
   - RAM <  16GB free: full del + reload from disk — safe, ~20-30s
   This gives instant model switching on 32GB+ machines while keeping
   16GB machines safe from OOM Killer.

Helper functions:
- _prepare_for_tex(): adaptive offload/delete based on RAM check
- _ensure_i23d_worker(): restore from CPU (fast) or disk (slow)
- _get_available_ram_gb(): reads /proc/meminfo
- _can_offload_to_cpu(): threshold check with logging

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-03-16 22:57:32 +08:00

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# 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.
# Apply torchvision compatibility fix before other imports
import sys
sys.path.insert(0, './hy3dshape')
sys.path.insert(0, './hy3dpaint')
try:
from torchvision_fix import apply_fix
apply_fix()
except ImportError:
print("Warning: torchvision_fix module not found, proceeding without compatibility fix")
except Exception as e:
print(f"Warning: Failed to apply torchvision fix: {e}")
import gc
import os
import random
import shutil
import subprocess
import time
from glob import glob
from pathlib import Path
import gradio as gr
import torch
import trimesh
import uvicorn
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
import uuid
import numpy as np
from hy3dshape.utils import logger
from hy3dpaint.convert_utils import create_glb_with_pbr_materials
# Globals for lazy load/unload
i23d_worker = None
tex_pipeline = None
tex_conf = None
MAX_SEED = 1e7
ENV = "Local" # "Huggingface"
if ENV == 'Huggingface':
"""
Setup environment for running on Huggingface platform.
This block performs the following:
- Changes directory to the differentiable renderer folder and runs a shell
script to compile the mesh painter.
- Installs a custom rasterizer wheel package via pip.
Note:
This setup assumes the script is running in the Huggingface environment
with the specified directory structure.
"""
import os, spaces, subprocess, sys, shlex
print("cd /home/user/app/hy3dgen/texgen/differentiable_renderer/ && bash compile_mesh_painter.sh")
os.system("cd /home/user/app/hy3dgen/texgen/differentiable_renderer/ && bash compile_mesh_painter.sh")
print('install custom')
subprocess.run(shlex.split("pip install custom_rasterizer-0.1-cp310-cp310-linux_x86_64.whl"),
check=True)
else:
"""
Define a dummy `spaces` module with a GPU decorator class for local environment.
The GPU decorator is a no-op that simply returns the decorated function unchanged.
This allows code that uses the `spaces.GPU` decorator to run without modification locally.
"""
class spaces:
class GPU:
def __init__(self, duration=60):
self.duration = duration
def __call__(self, func):
return func
def get_example_img_list():
"""
Load and return a sorted list of example image file paths.
Searches recursively for PNG images under the './assets/example_images/' directory.
Returns:
list[str]: Sorted list of file paths to example PNG images.
"""
print('Loading example img list ...')
return sorted(glob('./assets/example_images/**/*.png', recursive=True))
def get_example_txt_list():
"""
Load and return a list of example text prompts.
Reads lines from the './assets/example_prompts.txt' file, stripping whitespace.
Returns:
list[str]: List of example text prompts.
"""
print('Loading example txt list ...')
txt_list = list()
for line in open('./assets/example_prompts.txt', encoding='utf-8'):
txt_list.append(line.strip())
return txt_list
def gen_save_folder(max_size=200):
"""
Generate a new save folder inside SAVE_DIR, maintaining a maximum number of folders.
If the number of existing folders in SAVE_DIR exceeds `max_size`, the oldest folder is removed.
Args:
max_size (int, optional): Maximum number of folders to keep in SAVE_DIR. Defaults to 200.
Returns:
str: Path to the newly created save folder.
"""
os.makedirs(SAVE_DIR, exist_ok=True)
dirs = [f for f in Path(SAVE_DIR).iterdir() if f.is_dir()]
if len(dirs) >= max_size:
oldest_dir = min(dirs, key=lambda x: x.stat().st_ctime)
shutil.rmtree(oldest_dir)
print(f"Removed the oldest folder: {oldest_dir}")
new_folder = os.path.join(SAVE_DIR, str(uuid.uuid4()))
os.makedirs(new_folder, exist_ok=True)
print(f"Created new folder: {new_folder}")
return new_folder
# Removed complex PBR conversion functions - using simple trimesh-based conversion
def export_mesh(mesh, save_folder, textured=False, type='glb'):
"""
Export a mesh to a file in the specified folder, optionally including textures.
Args:
mesh (trimesh.Trimesh): The mesh object to export.
save_folder (str): Directory path where the mesh file will be saved.
textured (bool, optional): Whether to include textures/normals in the export. Defaults to False.
type (str, optional): File format to export ('glb' or 'obj' supported). Defaults to 'glb'.
Returns:
str: The full path to the exported mesh file.
"""
if textured:
path = os.path.join(save_folder, f'textured_mesh.{type}')
else:
path = os.path.join(save_folder, f'white_mesh.{type}')
if type not in ['glb', 'obj']:
mesh.export(path)
else:
mesh.export(path, include_normals=textured)
return path
def quick_convert_with_obj2gltf(obj_path: str, glb_path: str) -> bool:
# 执行转换
textures = {
'albedo': obj_path.replace('.obj', '.jpg'),
'metallic': obj_path.replace('.obj', '_metallic.jpg'),
'roughness': obj_path.replace('.obj', '_roughness.jpg')
}
create_glb_with_pbr_materials(obj_path, textures, glb_path)
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def build_model_viewer_html(save_folder, height=660, width=790, textured=False):
# Remove first folder from path to make relative path
if textured:
related_path = f"./textured_mesh.glb"
template_name = './assets/modelviewer-textured-template.html'
output_html_path = os.path.join(save_folder, f'textured_mesh.html')
else:
related_path = f"./white_mesh.glb"
template_name = './assets/modelviewer-template.html'
output_html_path = os.path.join(save_folder, f'white_mesh.html')
offset = 50 if textured else 10
with open(os.path.join(CURRENT_DIR, template_name), 'r', encoding='utf-8') as f:
template_html = f.read()
with open(output_html_path, 'w', encoding='utf-8') as f:
template_html = template_html.replace('#height#', f'{height - offset}')
template_html = template_html.replace('#width#', f'{width}')
template_html = template_html.replace('#src#', f'{related_path}/')
f.write(template_html)
rel_path = os.path.relpath(output_html_path, SAVE_DIR)
iframe_tag = f'<iframe src="/static/{rel_path}" \
height="{height}" width="100%" frameborder="0"></iframe>'
print(f'Find html file {output_html_path}, \
{os.path.exists(output_html_path)}, relative HTML path is /static/{rel_path}')
return f"""
<div style='height: {height}; width: 100%;'>
{iframe_tag}
</div>
"""
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# VRAM management helpers (used when --low_vram_mode is set)
#
# Adaptive strategy based on available system RAM:
#
# When switching from shape → texture (or vice versa):
# 1. Check available RAM via /proc/meminfo
# 2. If enough RAM to hold a model in CPU while loading the other (~17GB):
# → .to('cpu') the outgoing model (fast, no disk reload needed later)
# 3. If RAM is tight:
# → fully del the outgoing model, reload from disk later (~20-30s)
#
# This allows machines with ≥32GB RAM to swap models instantly,
# while 16GB machines safely fall back to disk reload.
# ---------------------------------------------------------------------------
# Approximate RAM required (GB) to hold one model in CPU while loading another.
# Model weights: ~7GB each. Loading from disk stages ~7GB temporarily.
# Total: 7 (existing in CPU) + 7 (loading new) + 2 (OS headroom) = 16GB.
_RAM_THRESHOLD_GB = 16.0
# Track whether i23d is offloaded to CPU RAM (vs deleted entirely).
_i23d_on_cpu = False
def _get_available_ram_gb():
"""Return available system RAM in GB from /proc/meminfo."""
try:
with open('/proc/meminfo') as f:
for line in f:
if line.startswith('MemAvailable:'):
return int(line.split()[1]) / (1024 * 1024)
except Exception:
pass
return 0.0
def _can_offload_to_cpu():
"""Check if there's enough RAM to keep a model in CPU while loading another."""
available = _get_available_ram_gb()
can = available >= _RAM_THRESHOLD_GB
logger.info(
f"RAM check: {available:.1f}GB available, "
f"need {_RAM_THRESHOLD_GB:.0f}GB for CPU offload → "
f"{'CPU offload (fast)' if can else 'full delete (safe)'}"
)
return can
def _prepare_for_tex():
"""Free VRAM from shape model before loading texture pipeline."""
global i23d_worker, _i23d_on_cpu
if i23d_worker is None:
_ensure_tex_pipeline()
return
if _can_offload_to_cpu():
logger.info("Offloading shape model to CPU RAM (fast path)...")
i23d_worker.to('cpu')
_i23d_on_cpu = True
torch.cuda.empty_cache()
else:
logger.info("Deleting shape model entirely (safe path, limited RAM)...")
del i23d_worker
i23d_worker = None
_i23d_on_cpu = False
gc.collect()
gc.collect()
torch.cuda.empty_cache()
_ensure_tex_pipeline()
def _ensure_i23d_worker():
"""Load shape model to GPU — from CPU RAM (fast) or disk (slow)."""
global i23d_worker, _i23d_on_cpu
if i23d_worker is not None and _i23d_on_cpu:
logger.info("Restoring shape model from CPU to GPU (fast path)...")
i23d_worker.to(args.device)
_i23d_on_cpu = False
elif i23d_worker is None:
logger.info("Reloading shape model from disk to GPU (slow path)...")
gc.collect()
torch.cuda.empty_cache()
from hy3dshape import Hunyuan3DDiTFlowMatchingPipeline
i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
args.model_path,
subfolder=args.subfolder,
use_safetensors=False,
device=args.device,
)
_i23d_on_cpu = False
# else: already on GPU, nothing to do
def _unload_tex_pipeline():
"""Delete texture pipeline entirely, freeing its VRAM."""
global tex_pipeline
if tex_pipeline is not None:
logger.info("Unloading texture pipeline from memory...")
del tex_pipeline
tex_pipeline = None
gc.collect()
gc.collect()
torch.cuda.empty_cache()
def _ensure_tex_pipeline():
"""Load texture pipeline to GPU if not already loaded."""
global tex_pipeline
if tex_pipeline is None and tex_conf is not None:
gc.collect()
torch.cuda.empty_cache()
from hy3dpaint.textureGenPipeline import Hunyuan3DPaintPipeline
logger.info("Loading texture pipeline to GPU...")
tex_pipeline = Hunyuan3DPaintPipeline(tex_conf)
@spaces.GPU(duration=60)
def _gen_shape(
caption=None,
image=None,
mv_image_front=None,
mv_image_back=None,
mv_image_left=None,
mv_image_right=None,
steps=50,
guidance_scale=7.5,
seed=1234,
octree_resolution=256,
check_box_rembg=False,
num_chunks=200000,
randomize_seed: bool = False,
):
if not MV_MODE and image is None and caption is None:
raise gr.Error("Please provide either a caption or an image.")
if MV_MODE:
if mv_image_front is None and mv_image_back is None \
and mv_image_left is None and mv_image_right is None:
raise gr.Error("Please provide at least one view image.")
image = {}
if mv_image_front:
image['front'] = mv_image_front
if mv_image_back:
image['back'] = mv_image_back
if mv_image_left:
image['left'] = mv_image_left
if mv_image_right:
image['right'] = mv_image_right
seed = int(randomize_seed_fn(seed, randomize_seed))
octree_resolution = int(octree_resolution)
if caption: print('prompt is', caption)
save_folder = gen_save_folder()
stats = {
'model': {
'shapegen': f'{args.model_path}/{args.subfolder}',
'texgen': f'{args.texgen_model_path}',
},
'params': {
'caption': caption,
'steps': steps,
'guidance_scale': guidance_scale,
'seed': seed,
'octree_resolution': octree_resolution,
'check_box_rembg': check_box_rembg,
'num_chunks': num_chunks,
}
}
time_meta = {}
if image is None:
start_time = time.time()
try:
image = t2i_worker(caption)
except Exception as e:
raise gr.Error(f"Text to 3D is disable. \
Please enable it by `python gradio_app.py --enable_t23d`.")
time_meta['text2image'] = time.time() - start_time
# remove disk io to make responding faster, uncomment at your will.
# image.save(os.path.join(save_folder, 'input.png'))
if MV_MODE:
start_time = time.time()
for k, v in image.items():
if check_box_rembg or v.mode == "RGB":
img = rmbg_worker(v.convert('RGB'))
image[k] = img
time_meta['remove background'] = time.time() - start_time
else:
if check_box_rembg or image.mode == "RGB":
start_time = time.time()
image = rmbg_worker(image.convert('RGB'))
time_meta['remove background'] = time.time() - start_time
# remove disk io to make responding faster, uncomment at your will.
# image.save(os.path.join(save_folder, 'rembg.png'))
# image to white model
start_time = time.time()
if args.low_vram_mode:
_ensure_i23d_worker()
generator = torch.Generator()
generator = generator.manual_seed(int(seed))
outputs = i23d_worker(
image=image,
num_inference_steps=steps,
guidance_scale=guidance_scale,
generator=generator,
octree_resolution=octree_resolution,
num_chunks=num_chunks,
output_type='mesh'
)
time_meta['shape generation'] = time.time() - start_time
logger.info("---Shape generation takes %s seconds ---" % (time.time() - start_time))
tmp_start = time.time()
mesh = export_to_trimesh(outputs)[0]
time_meta['export to trimesh'] = time.time() - tmp_start
stats['number_of_faces'] = mesh.faces.shape[0]
stats['number_of_vertices'] = mesh.vertices.shape[0]
stats['time'] = time_meta
main_image = image if not MV_MODE else image['front']
return mesh, main_image, save_folder, stats, seed
@spaces.GPU(duration=60)
def generation_all(
caption=None,
image=None,
mv_image_front=None,
mv_image_back=None,
mv_image_left=None,
mv_image_right=None,
steps=50,
guidance_scale=7.5,
seed=1234,
octree_resolution=256,
check_box_rembg=False,
num_chunks=200000,
randomize_seed: bool = False,
):
start_time_0 = time.time()
mesh, image, save_folder, stats, seed = _gen_shape(
caption,
image,
mv_image_front=mv_image_front,
mv_image_back=mv_image_back,
mv_image_left=mv_image_left,
mv_image_right=mv_image_right,
steps=steps,
guidance_scale=guidance_scale,
seed=seed,
octree_resolution=octree_resolution,
check_box_rembg=check_box_rembg,
num_chunks=num_chunks,
randomize_seed=randomize_seed,
)
path = export_mesh(mesh, save_folder, textured=False)
print(path)
print('='*40)
# tmp_time = time.time()
# mesh = floater_remove_worker(mesh)
# mesh = degenerate_face_remove_worker(mesh)
# logger.info("---Postprocessing takes %s seconds ---" % (time.time() - tmp_time))
# stats['time']['postprocessing'] = time.time() - tmp_time
tmp_time = time.time()
mesh = face_reduce_worker(mesh)
# path = export_mesh(mesh, save_folder, textured=False, type='glb')
path = export_mesh(mesh, save_folder, textured=False, type='obj') # 这样操作也会 core dump
logger.info("---Face Reduction takes %s seconds ---" % (time.time() - tmp_time))
stats['time']['face reduction'] = time.time() - tmp_time
tmp_time = time.time()
text_path = os.path.join(save_folder, f'textured_mesh.obj')
# In low_vram_mode: adaptively offload shape model (CPU or delete based on
# available RAM), then load texture pipeline.
if args.low_vram_mode:
_prepare_for_tex()
path_textured = tex_pipeline(mesh_path=path, image_path=image, output_mesh_path=text_path, save_glb=False)
# Unload texture pipeline after use so VRAM is free for the next shape request.
if args.low_vram_mode:
_unload_tex_pipeline()
logger.info("---Texture Generation takes %s seconds ---" % (time.time() - tmp_time))
stats['time']['texture generation'] = time.time() - tmp_time
tmp_time = time.time()
# Convert textured OBJ to GLB using obj2gltf with PBR support
glb_path_textured = os.path.join(save_folder, 'textured_mesh.glb')
conversion_success = quick_convert_with_obj2gltf(path_textured, glb_path_textured)
logger.info("---Convert textured OBJ to GLB takes %s seconds ---" % (time.time() - tmp_time))
stats['time']['convert textured OBJ to GLB'] = time.time() - tmp_time
stats['time']['total'] = time.time() - start_time_0
model_viewer_html_textured = build_model_viewer_html(save_folder,
height=HTML_HEIGHT,
width=HTML_WIDTH, textured=True)
if args.low_vram_mode:
torch.cuda.empty_cache()
return (
gr.update(value=path),
gr.update(value=glb_path_textured),
model_viewer_html_textured,
stats,
seed,
)
@spaces.GPU(duration=60)
def shape_generation(
caption=None,
image=None,
mv_image_front=None,
mv_image_back=None,
mv_image_left=None,
mv_image_right=None,
steps=50,
guidance_scale=7.5,
seed=1234,
octree_resolution=256,
check_box_rembg=False,
num_chunks=200000,
randomize_seed: bool = False,
):
start_time_0 = time.time()
mesh, image, save_folder, stats, seed = _gen_shape(
caption,
image,
mv_image_front=mv_image_front,
mv_image_back=mv_image_back,
mv_image_left=mv_image_left,
mv_image_right=mv_image_right,
steps=steps,
guidance_scale=guidance_scale,
seed=seed,
octree_resolution=octree_resolution,
check_box_rembg=check_box_rembg,
num_chunks=num_chunks,
randomize_seed=randomize_seed,
)
stats['time']['total'] = time.time() - start_time_0
mesh.metadata['extras'] = stats
path = export_mesh(mesh, save_folder, textured=False)
model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH)
if args.low_vram_mode:
torch.cuda.empty_cache()
return (
gr.update(value=path),
model_viewer_html,
stats,
seed,
)
def build_app():
title = 'Hunyuan3D-2: High Resolution Textured 3D Assets Generation'
if MV_MODE:
title = 'Hunyuan3D-2mv: Image to 3D Generation with 1-4 Views'
if 'mini' in args.subfolder:
title = 'Hunyuan3D-2mini: Strong 0.6B Image to Shape Generator'
title = 'Hunyuan-3D-2.1'
if TURBO_MODE:
title = title.replace(':', '-Turbo: Fast ')
title_html = f"""
<div style="font-size: 2em; font-weight: bold; text-align: center; margin-bottom: 5px">
{title}
</div>
<div align="center">
Tencent Hunyuan3D Team
</div>
"""
custom_css = """
.app.svelte-wpkpf6.svelte-wpkpf6:not(.fill_width) {
max-width: 1480px;
}
.mv-image button .wrap {
font-size: 10px;
}
.mv-image .icon-wrap {
width: 20px;
}
"""
with gr.Blocks(theme=gr.themes.Base(), title='Hunyuan-3D-2.1', analytics_enabled=False, css=custom_css) as demo:
gr.HTML(title_html)
with gr.Row():
with gr.Column(scale=3):
with gr.Tabs(selected='tab_img_prompt') as tabs_prompt:
with gr.Tab('Image Prompt', id='tab_img_prompt', visible=not MV_MODE) as tab_ip:
image = gr.Image(label='Image', type='pil', image_mode='RGBA', height=290)
caption = gr.State(None)
# with gr.Tab('Text Prompt', id='tab_txt_prompt', visible=HAS_T2I and not MV_MODE) as tab_tp:
# caption = gr.Textbox(label='Text Prompt',
# placeholder='HunyuanDiT will be used to generate image.',
# info='Example: A 3D model of a cute cat, white background')
with gr.Tab('MultiView Prompt', visible=MV_MODE) as tab_mv:
# gr.Label('Please upload at least one front image.')
with gr.Row():
mv_image_front = gr.Image(label='Front', type='pil', image_mode='RGBA', height=140,
min_width=100, elem_classes='mv-image')
mv_image_back = gr.Image(label='Back', type='pil', image_mode='RGBA', height=140,
min_width=100, elem_classes='mv-image')
with gr.Row():
mv_image_left = gr.Image(label='Left', type='pil', image_mode='RGBA', height=140,
min_width=100, elem_classes='mv-image')
mv_image_right = gr.Image(label='Right', type='pil', image_mode='RGBA', height=140,
min_width=100, elem_classes='mv-image')
with gr.Row():
btn = gr.Button(value='Gen Shape', variant='primary', min_width=100)
btn_all = gr.Button(value='Gen Textured Shape',
variant='primary',
visible=HAS_TEXTUREGEN,
min_width=100)
with gr.Group():
file_out = gr.File(label="File", visible=False)
file_out2 = gr.File(label="File", visible=False)
with gr.Tabs(selected='tab_options' if TURBO_MODE else 'tab_export'):
with gr.Tab("Options", id='tab_options', visible=TURBO_MODE):
gen_mode = gr.Radio(
label='Generation Mode',
info='Recommendation: Turbo for most cases, \
Fast for very complex cases, Standard seldom use.',
choices=['Turbo', 'Fast', 'Standard'],
value='Turbo')
decode_mode = gr.Radio(
label='Decoding Mode',
info='The resolution for exporting mesh from generated vectset',
choices=['Low', 'Standard', 'High'],
value='Standard')
with gr.Tab('Advanced Options', id='tab_advanced_options'):
with gr.Row():
check_box_rembg = gr.Checkbox(
value=True,
label='Remove Background',
min_width=100)
randomize_seed = gr.Checkbox(
label="Randomize seed",
value=True,
min_width=100)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=1234,
min_width=100,
)
with gr.Row():
num_steps = gr.Slider(maximum=100,
minimum=1,
value=5 if 'turbo' in args.subfolder else 30,
step=1, label='Inference Steps')
octree_resolution = gr.Slider(maximum=512,
minimum=16,
value=256,
label='Octree Resolution')
with gr.Row():
cfg_scale = gr.Number(value=5.0, label='Guidance Scale', min_width=100)
num_chunks = gr.Slider(maximum=5000000, minimum=1000, value=8000,
label='Number of Chunks', min_width=100)
with gr.Tab("Export", id='tab_export'):
with gr.Row():
file_type = gr.Dropdown(label='File Type',
choices=SUPPORTED_FORMATS,
value='glb', min_width=100)
reduce_face = gr.Checkbox(label='Simplify Mesh',
value=False, min_width=100)
export_texture = gr.Checkbox(label='Include Texture', value=False,
visible=False, min_width=100)
target_face_num = gr.Slider(maximum=1000000, minimum=100, value=10000,
label='Target Face Number')
with gr.Row():
confirm_export = gr.Button(value="Transform", min_width=100)
file_export = gr.DownloadButton(label="Download", variant='primary',
interactive=False, min_width=100)
with gr.Column(scale=6):
with gr.Tabs(selected='gen_mesh_panel') as tabs_output:
with gr.Tab('Generated Mesh', id='gen_mesh_panel'):
html_gen_mesh = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
with gr.Tab('Exporting Mesh', id='export_mesh_panel'):
html_export_mesh = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
with gr.Tab('Mesh Statistic', id='stats_panel'):
stats = gr.Json({}, label='Mesh Stats')
with gr.Column(scale=3 if MV_MODE else 2):
with gr.Tabs(selected='tab_img_gallery') as gallery:
with gr.Tab('Image to 3D Gallery',
id='tab_img_gallery',
visible=not MV_MODE) as tab_gi:
with gr.Row():
gr.Examples(examples=example_is, inputs=[image],
label=None, examples_per_page=18)
tab_ip.select(fn=lambda: gr.update(selected='tab_img_gallery'), outputs=gallery)
#if HAS_T2I:
# tab_tp.select(fn=lambda: gr.update(selected='tab_txt_gallery'), outputs=gallery)
btn.click(
shape_generation,
inputs=[
caption,
image,
mv_image_front,
mv_image_back,
mv_image_left,
mv_image_right,
num_steps,
cfg_scale,
seed,
octree_resolution,
check_box_rembg,
num_chunks,
randomize_seed,
],
outputs=[file_out, html_gen_mesh, stats, seed]
).then(
lambda: (gr.update(visible=False, value=False), gr.update(interactive=True), gr.update(interactive=True),
gr.update(interactive=False)),
outputs=[export_texture, reduce_face, confirm_export, file_export],
).then(
lambda: gr.update(selected='gen_mesh_panel'),
outputs=[tabs_output],
)
btn_all.click(
generation_all,
inputs=[
caption,
image,
mv_image_front,
mv_image_back,
mv_image_left,
mv_image_right,
num_steps,
cfg_scale,
seed,
octree_resolution,
check_box_rembg,
num_chunks,
randomize_seed,
],
outputs=[file_out, file_out2, html_gen_mesh, stats, seed]
).then(
lambda: (gr.update(visible=True, value=True), gr.update(interactive=False), gr.update(interactive=True),
gr.update(interactive=False)),
outputs=[export_texture, reduce_face, confirm_export, file_export],
).then(
lambda: gr.update(selected='gen_mesh_panel'),
outputs=[tabs_output],
)
def on_gen_mode_change(value):
if value == 'Turbo':
return gr.update(value=5)
elif value == 'Fast':
return gr.update(value=10)
else:
return gr.update(value=30)
gen_mode.change(on_gen_mode_change, inputs=[gen_mode], outputs=[num_steps])
def on_decode_mode_change(value):
if value == 'Low':
return gr.update(value=196)
elif value == 'Standard':
return gr.update(value=256)
else:
return gr.update(value=384)
decode_mode.change(on_decode_mode_change, inputs=[decode_mode],
outputs=[octree_resolution])
def on_export_click(file_out, file_out2, file_type,
reduce_face, export_texture, target_face_num):
if file_out is None:
raise gr.Error('Please generate a mesh first.')
print(f'exporting {file_out}')
print(f'reduce face to {target_face_num}')
if export_texture:
mesh = trimesh.load(file_out2)
save_folder = gen_save_folder()
path = export_mesh(mesh, save_folder, textured=True, type=file_type)
# for preview
save_folder = gen_save_folder()
_ = export_mesh(mesh, save_folder, textured=True)
model_viewer_html = build_model_viewer_html(save_folder,
height=HTML_HEIGHT,
width=HTML_WIDTH,
textured=True)
else:
mesh = trimesh.load(file_out)
mesh = floater_remove_worker(mesh)
mesh = degenerate_face_remove_worker(mesh)
if reduce_face:
mesh = face_reduce_worker(mesh, target_face_num)
save_folder = gen_save_folder()
path = export_mesh(mesh, save_folder, textured=False, type=file_type)
# for preview
save_folder = gen_save_folder()
_ = export_mesh(mesh, save_folder, textured=False)
model_viewer_html = build_model_viewer_html(save_folder,
height=HTML_HEIGHT,
width=HTML_WIDTH,
textured=False)
print(f'export to {path}')
return model_viewer_html, gr.update(value=path, interactive=True)
confirm_export.click(
lambda: gr.update(selected='export_mesh_panel'),
outputs=[tabs_output],
).then(
on_export_click,
inputs=[file_out, file_out2, file_type, reduce_face, export_texture, target_face_num],
outputs=[html_export_mesh, file_export]
)
return demo
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2.1')
parser.add_argument("--subfolder", type=str, default='hunyuan3d-dit-v2-1')
parser.add_argument("--texgen_model_path", type=str, default='tencent/Hunyuan3D-2.1')
parser.add_argument('--port', type=int, default=8080)
parser.add_argument('--host', type=str, default='0.0.0.0')
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--mc_algo', type=str, default='mc')
parser.add_argument('--cache-path', type=str, default='./save_dir')
parser.add_argument('--enable_t23d', action='store_true')
parser.add_argument('--disable_tex', action='store_true')
parser.add_argument('--enable_flashvdm', action='store_true')
parser.add_argument('--compile', action='store_true')
parser.add_argument('--low_vram_mode', action='store_true')
args = parser.parse_args()
SAVE_DIR = args.cache_path
os.makedirs(SAVE_DIR, exist_ok=True)
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
MV_MODE = 'mv' in args.model_path
TURBO_MODE = 'turbo' in args.subfolder
HTML_HEIGHT = 690 if MV_MODE else 650
HTML_WIDTH = 500
HTML_OUTPUT_PLACEHOLDER = f"""
<div style='height: {650}px; width: 100%; border-radius: 8px; border-color: #e5e7eb; border-style: solid; border-width: 1px; display: flex; justify-content: center; align-items: center;'>
<div style='text-align: center; font-size: 16px; color: #6b7280;'>
<p style="color: #8d8d8d;">Welcome to Hunyuan3D!</p>
<p style="color: #8d8d8d;">No mesh here.</p>
</div>
</div>
"""
INPUT_MESH_HTML = """
<div style='height: 490px; width: 100%; border-radius: 8px;
border-color: #e5e7eb; order-style: solid; border-width: 1px;'>
</div>
"""
example_is = get_example_img_list()
example_ts = get_example_txt_list()
SUPPORTED_FORMATS = ['glb', 'obj', 'ply', 'stl']
HAS_TEXTUREGEN = False
if not args.disable_tex:
try:
# Apply torchvision fix before importing basicsr/RealESRGAN
print("Applying torchvision compatibility fix for texture generation...")
try:
from torchvision_fix import apply_fix
fix_result = apply_fix()
if not fix_result:
print("Warning: Torchvision fix may not have been applied successfully")
except Exception as fix_error:
print(f"Warning: Failed to apply torchvision fix: {fix_error}")
# from hy3dgen.texgen import Hunyuan3DPaintPipeline
# texgen_worker = Hunyuan3DPaintPipeline.from_pretrained(args.texgen_model_path)
# if args.low_vram_mode:
# texgen_worker.enable_model_cpu_offload()
from hy3dpaint.textureGenPipeline import Hunyuan3DPaintPipeline, Hunyuan3DPaintConfig
tex_conf = Hunyuan3DPaintConfig(max_num_view=9, resolution=512)
tex_conf.realesrgan_ckpt_path = "hy3dpaint/ckpt/RealESRGAN_x4plus.pth"
tex_conf.multiview_cfg_path = "hy3dpaint/cfgs/hunyuan-paint-pbr.yaml"
tex_conf.custom_pipeline = "hy3dpaint/hunyuanpaintpbr"
if not args.low_vram_mode:
# Load immediately; in low_vram_mode we load on-demand per request.
tex_pipeline = Hunyuan3DPaintPipeline(tex_conf)
# Not help much, ignore for now.
# if args.compile:
# texgen_worker.models['delight_model'].pipeline.unet.compile()
# texgen_worker.models['delight_model'].pipeline.vae.compile()
# texgen_worker.models['multiview_model'].pipeline.unet.compile()
# texgen_worker.models['multiview_model'].pipeline.vae.compile()
HAS_TEXTUREGEN = True
except Exception as e:
import traceback
traceback.print_exc()
print(f"Error loading texture generator: {e}")
print("Failed to load texture generator.")
print('Please try to install requirements by following README.md')
HAS_TEXTUREGEN = False
HAS_T2I = True
if args.enable_t23d:
from hy3dgen.text2image import HunyuanDiTPipeline
t2i_worker = HunyuanDiTPipeline('Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled')
HAS_T2I = True
from hy3dshape import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier, \
Hunyuan3DDiTFlowMatchingPipeline
from hy3dshape.pipelines import export_to_trimesh
from hy3dshape.rembg import BackgroundRemover
rmbg_worker = BackgroundRemover()
if args.low_vram_mode:
# Defer i23d loading to first request — saves ~7.25GB VRAM at startup
# and avoids keeping it in RAM while tex pipeline loads.
logger.info("low_vram_mode: shape model will be loaded on first request")
else:
i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
args.model_path,
subfolder=args.subfolder,
use_safetensors=False,
device=args.device,
)
if args.enable_flashvdm:
mc_algo = 'mc' if args.device in ['cpu', 'mps'] else args.mc_algo
i23d_worker.enable_flashvdm(mc_algo=mc_algo)
if args.compile:
i23d_worker.compile()
floater_remove_worker = FloaterRemover()
degenerate_face_remove_worker = DegenerateFaceRemover()
face_reduce_worker = FaceReducer()
# https://discuss.huggingface.co/t/how-to-serve-an-html-file/33921/2
# create a FastAPI app
app = FastAPI()
# create a static directory to store the static files
static_dir = Path(SAVE_DIR).absolute()
static_dir.mkdir(parents=True, exist_ok=True)
app.mount("/static", StaticFiles(directory=static_dir, html=True), name="static")
shutil.copytree('./assets/env_maps', os.path.join(static_dir, 'env_maps'), dirs_exist_ok=True)
if args.low_vram_mode:
torch.cuda.empty_cache()
demo = build_app()
app = gr.mount_gradio_app(app, demo, path="/")
uvicorn.run(app, host=args.host, port=args.port)
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