', obj_html))
+
+ output_html_path = output_html_path.replace(SAVE_DIR + '/', '')
+ iframe_tag = f''
+ print(f'Find html {output_html_path}, {os.path.exists(output_html_path)}')
+
+ return f"""
+
+ {iframe_tag}
+
+ """
+
+
+def _gen_shape(
+ caption,
+ image,
+ steps=50,
+ guidance_scale=7.5,
+ seed=1234,
+ octree_resolution=256,
+ check_box_rembg=False,
+):
+ if caption: print('prompt is', caption)
+ save_folder = gen_save_folder()
+ stats = {}
+ time_meta = {}
+ start_time_0 = time.time()
+
+ 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
+
+ image.save(os.path.join(save_folder, 'input.png'))
+
+ print(image.mode)
+ if check_box_rembg or image.mode == "RGB":
+ start_time = time.time()
+ image = rmbg_worker(image.convert('RGB'))
+ time_meta['rembg'] = time.time() - start_time
+
+ image.save(os.path.join(save_folder, 'rembg.png'))
+
+ # image to white model
+ start_time = time.time()
+
+ generator = torch.Generator()
+ generator = generator.manual_seed(int(seed))
+ mesh = i23d_worker(
+ image=image,
+ num_inference_steps=steps,
+ guidance_scale=guidance_scale,
+ generator=generator,
+ octree_resolution=octree_resolution
+ )[0]
+
+ mesh = FloaterRemover()(mesh)
+ mesh = DegenerateFaceRemover()(mesh)
+ mesh = FaceReducer()(mesh)
+
+ stats['number_of_faces'] = mesh.faces.shape[0]
+ stats['number_of_vertices'] = mesh.vertices.shape[0]
+
+ time_meta['image_to_textured_3d'] = {'total': time.time() - start_time}
+ time_meta['total'] = time.time() - start_time_0
+ stats['time'] = time_meta
+ return mesh, save_folder
+
+
+def generation_all(
+ caption,
+ image,
+ steps=50,
+ guidance_scale=7.5,
+ seed=1234,
+ octree_resolution=256,
+ check_box_rembg=False
+):
+ mesh, save_folder = _gen_shape(
+ caption,
+ image,
+ steps=steps,
+ guidance_scale=guidance_scale,
+ seed=seed,
+ octree_resolution=octree_resolution,
+ check_box_rembg=check_box_rembg
+ )
+ path = export_mesh(mesh, save_folder, textured=False)
+ model_viewer_html = build_model_viewer_html(save_folder, height=596, width=700)
+
+ textured_mesh = texgen_worker(mesh, image)
+ path_textured = export_mesh(textured_mesh, save_folder, textured=True)
+ model_viewer_html_textured = build_model_viewer_html(save_folder, height=596, width=700, textured=True)
+
+ return (
+ gr.update(value=path, visible=True),
+ gr.update(value=path_textured, visible=True),
+ model_viewer_html,
+ model_viewer_html_textured,
+ )
+
+
+def shape_generation(
+ caption,
+ image,
+ steps=50,
+ guidance_scale=7.5,
+ seed=1234,
+ octree_resolution=256,
+ check_box_rembg=False,
+):
+ mesh, save_folder = _gen_shape(
+ caption,
+ image,
+ steps=steps,
+ guidance_scale=guidance_scale,
+ seed=seed,
+ octree_resolution=octree_resolution,
+ check_box_rembg=check_box_rembg
+ )
+
+ path = export_mesh(mesh, save_folder, textured=False)
+ model_viewer_html = build_model_viewer_html(save_folder, height=596, width=700)
+
+ return (
+ gr.update(value=path, visible=True),
+ model_viewer_html,
+ )
+
+
+def build_app():
+ title_html = """
+
+
+ Hunyuan3D-2: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
+
+
+ Tencent Hunyuan3D Team
+
+
+ """
+
+ with gr.Blocks(theme=gr.themes.Base(), title='Hunyuan-3D-2.0') as demo:
+ gr.HTML(title_html)
+
+ with gr.Row():
+ with gr.Column(scale=2):
+ with gr.Tabs() as tabs_prompt:
+ with gr.Tab('Image Prompt', id='tab_img_prompt') as tab_ip:
+ image = gr.Image(label='Image', type='pil', image_mode='RGBA', height=290)
+ with gr.Row():
+ check_box_rembg = gr.Checkbox(value=True, label='Remove Background')
+
+ with gr.Tab('Text Prompt', id='tab_txt_prompt', visible=HAS_T2I) 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.Accordion('Advanced Options', open=False):
+ num_steps = gr.Slider(maximum=50, minimum=20, value=30, step=1, label='Inference Steps')
+ octree_resolution = gr.Dropdown([256, 384, 512], value=256, label='Octree Resolution')
+ cfg_scale = gr.Number(value=5.5, label='Guidance Scale')
+ seed = gr.Slider(maximum=1e7, minimum=0, value=1234, label='Seed')
+
+ with gr.Group():
+ btn = gr.Button(value='Generate Shape Only', variant='primary')
+ btn_all = gr.Button(value='Generate Shape and Texture', variant='primary', visible=HAS_TEXTUREGEN)
+
+ with gr.Group():
+ file_out = gr.File(label="File", visible=False)
+ file_out2 = gr.File(label="File", visible=False)
+
+ with gr.Column(scale=5):
+ with gr.Tabs():
+ with gr.Tab('Generated Mesh') as mesh1:
+ html_output1 = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
+ with gr.Tab('Generated Textured Mesh') as mesh2:
+ html_output2 = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
+
+ with gr.Column(scale=2):
+ with gr.Tabs() as gallery:
+ with gr.Tab('Image to 3D Gallery', id='tab_img_gallery') as tab_gi:
+ with gr.Row():
+ gr.Examples(examples=example_is, inputs=[image],
+ label="Image Prompts", examples_per_page=18)
+
+ with gr.Tab('Text to 3D Gallery', id='tab_txt_gallery', visible=HAS_T2I) as tab_gt:
+ with gr.Row():
+ gr.Examples(examples=example_ts, inputs=[caption],
+ label="Text Prompts", examples_per_page=18)
+
+ if not HAS_TEXTUREGEN:
+ gr.HTML(""")
+
+ Warning:
+ Texture synthesis is disable due to missing requirements,
+ please install requirements following README.md to activate it.
+
+ """)
+ if not args.enable_t23d:
+ gr.HTML("""
+
+ Warning:
+ Text to 3D is disable. To activate it, please run `python gradio_app.py --enable_t23d`.
+
+ """)
+
+ tab_gi.select(fn=lambda: gr.update(selected='tab_img_prompt'), outputs=tabs_prompt)
+ if HAS_T2I:
+ tab_gt.select(fn=lambda: gr.update(selected='tab_txt_prompt'), outputs=tabs_prompt)
+
+ btn.click(
+ shape_generation,
+ inputs=[
+ caption,
+ image,
+ num_steps,
+ cfg_scale,
+ seed,
+ octree_resolution,
+ check_box_rembg,
+ ],
+ outputs=[file_out, html_output1]
+ ).then(
+ lambda: gr.update(visible=True),
+ outputs=[file_out],
+ )
+
+ btn_all.click(
+ generation_all,
+ inputs=[
+ caption,
+ image,
+ num_steps,
+ cfg_scale,
+ seed,
+ octree_resolution,
+ check_box_rembg,
+ ],
+ outputs=[file_out, file_out2, html_output1, html_output2]
+ ).then(
+ lambda: (gr.update(visible=True), gr.update(visible=True)),
+ outputs=[file_out, file_out2],
+ )
+
+ return demo
+
+
+if __name__ == '__main__':
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--port', type=int, default=8080)
+ parser.add_argument('--cache-path', type=str, default='gradio_cache')
+ parser.add_argument('--enable_t23d', 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__))
+
+ HTML_OUTPUT_PLACEHOLDER = """
+
+ """
+
+ INPUT_MESH_HTML = """
+
+
+ """
+ example_is = get_example_img_list()
+ example_ts = get_example_txt_list()
+
+ try:
+ from hy3dgen.texgen import Hunyuan3DPaintPipeline
+
+ texgen_worker = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2')
+ HAS_TEXTUREGEN = True
+ except Exception as e:
+ print(e)
+ print("Failed to load texture generator.")
+ print('Please try to install requirements by following README.md')
+ HAS_TEXTUREGEN = False
+
+ HAS_T2I = False
+ if args.enable_t23d:
+ from hy3dgen.text2image import HunyuanDiTPipeline
+
+ t2i_worker = HunyuanDiTPipeline('Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled')
+ HAS_T2I = True
+
+ from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, \
+ Hunyuan3DDiTFlowMatchingPipeline
+ from hy3dgen.rembg import BackgroundRemover
+
+ rmbg_worker = BackgroundRemover()
+ i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
+ 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('./gradio_cache')
+ static_dir.mkdir(parents=True, exist_ok=True)
+ app.mount("/static", StaticFiles(directory=static_dir), name="static")
+
+ demo = build_app()
+ app = gr.mount_gradio_app(app, demo, path="/")
+ uvicorn.run(app, host="0.0.0.0", port=args.port)
diff --git a/.ipynb_checkpoints/hg_app-checkpoint.py b/.ipynb_checkpoints/hg_app-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..41d691e119a36d0d4460f9fa287e36e667bc3a5e
--- /dev/null
+++ b/.ipynb_checkpoints/hg_app-checkpoint.py
@@ -0,0 +1,416 @@
+import os
+import spaces
+import subprocess
+def install_cuda_toolkit():
+ # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run"
+ CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
+ CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
+ subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
+ subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
+ subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])
+
+ os.environ["CUDA_HOME"] = "/usr/local/cuda"
+ os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
+ os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
+ os.environ["CUDA_HOME"],
+ "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
+ )
+ # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
+ os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
+
+install_cuda_toolkit()
+os.system("cd /home/user/app/hy3dgen/texgen/differentiable_renderer/ && bash compile_mesh_painter.sh")
+os.system("cd /home/user/app/hy3dgen/texgen/custom_rasterizer && pip install .")
+
+import os
+import shutil
+import time
+from glob import glob
+from pathlib import Path
+
+import gradio as gr
+import torch
+import uvicorn
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+
+
+def get_example_img_list():
+ print('Loading example img list ...')
+ return sorted(glob('./assets/example_images/*.png'))
+
+
+def get_example_txt_list():
+ print('Loading example txt list ...')
+ txt_list = list()
+ for line in open('./assets/example_prompts.txt'):
+ txt_list.append(line.strip())
+ return txt_list
+
+
+def gen_save_folder(max_size=60):
+ os.makedirs(SAVE_DIR, exist_ok=True)
+ exists = set(int(_) for _ in os.listdir(SAVE_DIR) if not _.startswith("."))
+ cur_id = min(set(range(max_size)) - exists) if len(exists) < max_size else -1
+ if os.path.exists(f"{SAVE_DIR}/{(cur_id + 1) % max_size}"):
+ shutil.rmtree(f"{SAVE_DIR}/{(cur_id + 1) % max_size}")
+ print(f"remove {SAVE_DIR}/{(cur_id + 1) % max_size} success !!!")
+ save_folder = f"{SAVE_DIR}/{max(0, cur_id)}"
+ os.makedirs(save_folder, exist_ok=True)
+ print(f"mkdir {save_folder} suceess !!!")
+ return save_folder
+
+
+def export_mesh(mesh, save_folder, textured=False):
+ if textured:
+ path = os.path.join(save_folder, f'textured_mesh.glb')
+ else:
+ path = os.path.join(save_folder, f'white_mesh.glb')
+ mesh.export(path, include_normals=textured)
+ return path
+
+
+def build_model_viewer_html(save_folder, height=660, width=790, textured=False):
+ 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')
+
+ with open(os.path.join(CURRENT_DIR, template_name), 'r') as f:
+ template_html = f.read()
+ obj_html = f"""
+
+
+
+
+ """
+
+ with open(output_html_path, 'w') as f:
+ f.write(template_html.replace('', obj_html))
+
+ output_html_path = output_html_path.replace(SAVE_DIR + '/', '')
+ iframe_tag = f''
+ print(f'Find html {output_html_path}, {os.path.exists(output_html_path)}')
+
+ return f"""
+
+ {iframe_tag}
+
+ """
+
+@spaces.GPU(duration=40)
+def _gen_shape(
+ caption,
+ image,
+ steps=50,
+ guidance_scale=7.5,
+ seed=1234,
+ octree_resolution=256,
+ check_box_rembg=False,
+):
+ if caption: print('prompt is', caption)
+ save_folder = gen_save_folder()
+ stats = {}
+ time_meta = {}
+ start_time_0 = time.time()
+
+ 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
+
+ image.save(os.path.join(save_folder, 'input.png'))
+
+ print(image.mode)
+ if check_box_rembg or image.mode == "RGB":
+ start_time = time.time()
+ image = rmbg_worker(image.convert('RGB'))
+ time_meta['rembg'] = time.time() - start_time
+
+ image.save(os.path.join(save_folder, 'rembg.png'))
+
+ # image to white model
+ start_time = time.time()
+
+ generator = torch.Generator()
+ generator = generator.manual_seed(int(seed))
+ mesh = i23d_worker(
+ image=image,
+ num_inference_steps=steps,
+ guidance_scale=guidance_scale,
+ generator=generator,
+ octree_resolution=octree_resolution
+ )[0]
+
+ mesh = FloaterRemover()(mesh)
+ mesh = DegenerateFaceRemover()(mesh)
+ mesh = FaceReducer()(mesh)
+
+ stats['number_of_faces'] = mesh.faces.shape[0]
+ stats['number_of_vertices'] = mesh.vertices.shape[0]
+
+ time_meta['image_to_textured_3d'] = {'total': time.time() - start_time}
+ time_meta['total'] = time.time() - start_time_0
+ stats['time'] = time_meta
+ return mesh, save_folder
+
+@spaces.GPU(duration=60)
+def generation_all(
+ caption,
+ image,
+ steps=50,
+ guidance_scale=7.5,
+ seed=1234,
+ octree_resolution=256,
+ check_box_rembg=False
+):
+ mesh, save_folder = _gen_shape(
+ caption,
+ image,
+ steps=steps,
+ guidance_scale=guidance_scale,
+ seed=seed,
+ octree_resolution=octree_resolution,
+ check_box_rembg=check_box_rembg
+ )
+ path = export_mesh(mesh, save_folder, textured=False)
+ model_viewer_html = build_model_viewer_html(save_folder, height=596, width=700)
+
+ textured_mesh = texgen_worker(mesh, image)
+ path_textured = export_mesh(textured_mesh, save_folder, textured=True)
+ model_viewer_html_textured = build_model_viewer_html(save_folder, height=596, width=700, textured=True)
+
+ return (
+ gr.update(value=path, visible=True),
+ gr.update(value=path_textured, visible=True),
+ model_viewer_html,
+ model_viewer_html_textured,
+ )
+
+@spaces.GPU(duration=40)
+def shape_generation(
+ caption,
+ image,
+ steps=50,
+ guidance_scale=7.5,
+ seed=1234,
+ octree_resolution=256,
+ check_box_rembg=False,
+):
+ mesh, save_folder = _gen_shape(
+ caption,
+ image,
+ steps=steps,
+ guidance_scale=guidance_scale,
+ seed=seed,
+ octree_resolution=octree_resolution,
+ check_box_rembg=check_box_rembg
+ )
+
+ path = export_mesh(mesh, save_folder, textured=False)
+ model_viewer_html = build_model_viewer_html(save_folder, height=596, width=700)
+
+ return (
+ gr.update(value=path, visible=True),
+ model_viewer_html,
+ )
+
+
+def build_app():
+ title_html = """
+
+
+ Hunyuan3D-2: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
+
+
+ Tencent Hunyuan3D Team
+
+
+ """
+
+ with gr.Blocks(theme=gr.themes.Base(), title='Hunyuan-3D-2.0') as demo:
+ gr.HTML(title_html)
+
+ with gr.Row():
+ with gr.Column(scale=2):
+ with gr.Tabs() as tabs_prompt:
+ with gr.Tab('Image Prompt', id='tab_img_prompt') as tab_ip:
+ image = gr.Image(label='Image', type='pil', image_mode='RGBA', height=290)
+ with gr.Row():
+ check_box_rembg = gr.Checkbox(value=True, label='Remove Background')
+
+ with gr.Tab('Text Prompt', id='tab_txt_prompt', visible=HAS_T2I) 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.Accordion('Advanced Options', open=False):
+ num_steps = gr.Slider(maximum=50, minimum=20, value=30, step=1, label='Inference Steps')
+ octree_resolution = gr.Dropdown([256, 384, 512], value=256, label='Octree Resolution')
+ cfg_scale = gr.Number(value=5.5, label='Guidance Scale')
+ seed = gr.Slider(maximum=1e7, minimum=0, value=1234, label='Seed')
+
+ with gr.Group():
+ btn = gr.Button(value='Generate Shape Only', variant='primary')
+ btn_all = gr.Button(value='Generate Shape and Texture', variant='primary', visible=HAS_TEXTUREGEN)
+
+ with gr.Group():
+ file_out = gr.File(label="File", visible=False)
+ file_out2 = gr.File(label="File", visible=False)
+
+ with gr.Column(scale=5):
+ with gr.Tabs():
+ with gr.Tab('Generated Mesh') as mesh1:
+ html_output1 = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
+ with gr.Tab('Generated Textured Mesh') as mesh2:
+ html_output2 = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
+
+ with gr.Column(scale=2):
+ with gr.Tabs() as gallery:
+ with gr.Tab('Image to 3D Gallery', id='tab_img_gallery') as tab_gi:
+ with gr.Row():
+ gr.Examples(examples=example_is, inputs=[image],
+ label="Image Prompts", examples_per_page=18)
+
+ with gr.Tab('Text to 3D Gallery', id='tab_txt_gallery', visible=HAS_T2I) as tab_gt:
+ with gr.Row():
+ gr.Examples(examples=example_ts, inputs=[caption],
+ label="Text Prompts", examples_per_page=18)
+
+ if not HAS_TEXTUREGEN:
+ gr.HTML(""")
+
+ Warning:
+ Texture synthesis is disable due to missing requirements,
+ please install requirements following README.md to activate it.
+
+ """)
+ if not args.enable_t23d:
+ gr.HTML("""
+
+ Warning:
+ Text to 3D is disable. To activate it, please run `python gradio_app.py --enable_t23d`.
+
+ """)
+
+ tab_gi.select(fn=lambda: gr.update(selected='tab_img_prompt'), outputs=tabs_prompt)
+ if HAS_T2I:
+ tab_gt.select(fn=lambda: gr.update(selected='tab_txt_prompt'), outputs=tabs_prompt)
+
+ btn.click(
+ shape_generation,
+ inputs=[
+ caption,
+ image,
+ num_steps,
+ cfg_scale,
+ seed,
+ octree_resolution,
+ check_box_rembg,
+ ],
+ outputs=[file_out, html_output1]
+ ).then(
+ lambda: gr.update(visible=True),
+ outputs=[file_out],
+ )
+
+ btn_all.click(
+ generation_all,
+ inputs=[
+ caption,
+ image,
+ num_steps,
+ cfg_scale,
+ seed,
+ octree_resolution,
+ check_box_rembg,
+ ],
+ outputs=[file_out, file_out2, html_output1, html_output2]
+ ).then(
+ lambda: (gr.update(visible=True), gr.update(visible=True)),
+ outputs=[file_out, file_out2],
+ )
+
+ return demo
+
+
+if __name__ == '__main__':
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--port', type=int, default=8080)
+ parser.add_argument('--cache-path', type=str, default='gradio_cache')
+ parser.add_argument('--enable_t23d', default=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__))
+
+ HTML_OUTPUT_PLACEHOLDER = """
+
+ """
+
+ INPUT_MESH_HTML = """
+
+
+ """
+ example_is = get_example_img_list()
+ example_ts = get_example_txt_list()
+
+ try:
+ from hy3dgen.texgen import Hunyuan3DPaintPipeline
+
+ texgen_worker = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2')
+ HAS_TEXTUREGEN = True
+ except Exception as e:
+ print(e)
+ print("Failed to load texture generator.")
+ print('Please try to install requirements by following README.md')
+ HAS_TEXTUREGEN = False
+
+ HAS_T2I = False
+ if args.enable_t23d:
+ from hy3dgen.text2image import HunyuanDiTPipeline
+
+ t2i_worker = HunyuanDiTPipeline('Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled')
+ HAS_T2I = True
+
+ from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, \
+ Hunyuan3DDiTFlowMatchingPipeline
+ from hy3dgen.rembg import BackgroundRemover
+
+ rmbg_worker = BackgroundRemover()
+ i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
+ 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('./gradio_cache')
+ static_dir.mkdir(parents=True, exist_ok=True)
+ app.mount("/static", StaticFiles(directory=static_dir), name="static")
+
+ demo = build_app()
+ app = gr.mount_gradio_app(app, demo, path="/")
+ uvicorn.run(app, host="0.0.0.0", port=7860)
diff --git a/.ipynb_checkpoints/requirements-checkpoint.txt b/.ipynb_checkpoints/requirements-checkpoint.txt
new file mode 100644
index 0000000000000000000000000000000000000000..434962eece0c4691e11152c73e063c8a130cdd99
--- /dev/null
+++ b/.ipynb_checkpoints/requirements-checkpoint.txt
@@ -0,0 +1,35 @@
+gradio_litmodel3d
+ninja
+pybind11
+trimesh
+diffusers
+tqdm
+einops
+opencv-python
+numpy
+torch
+transformers
+torchvision
+torchaudio
+ConfigArgParse
+xatlas
+scikit-learn
+scikit-image
+tritonclient
+gevent
+geventhttpclient
+facexlib
+accelerate
+ipdb
+omegaconf
+pymeshlab
+pytorch_lightning
+taming-transformers-rom1504
+kornia
+rembg
+onnxruntime
+pygltflib
+sentencepiece
+gradio
+uvicorn
+fastapi
\ No newline at end of file
diff --git a/assets/modelviewer-template.html b/assets/modelviewer-template.html
index e061161a62636aa33561112face6cef52e7faa45..5a81985e2ccc115efdb2848523da731945bb0957 100644
--- a/assets/modelviewer-template.html
+++ b/assets/modelviewer-template.html
@@ -3,7 +3,8 @@
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 
+ 
+ 
+
+
)
+
+[//]: # ( 
)
+
+[//]: # ( 
)
+
+
+
+
+ 
+