Upload app.py

app.py

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+# MIT License
+
+# Copyright (c) Microsoft
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# Copyright (c) [2025] [Microsoft]
+# Copyright (c) [2025] [Chongjie Ye] 
+# SPDX-License-Identifier: MIT
+# This file has been modified by Chongjie Ye on 2025/04/10
+# Original file was released under MIT, with the full license text # available at https://github.com/atong01/conditional-flow-matching/blob/1.0.7/LICENSE.
+# This modified file is released under the same license.
+
+import gradio as gr
+import os
+os.environ['SPCONV_ALGO'] = 'native'
+from typing import *
+import torch
+import numpy as np
+import tempfile
+
+import zipfile
+
+# ---------------------------------------------------------------------------
+# NOTE
+# The original Hi3DGen implementation expects the `hi3dgen` Python package to
+# reside alongside this app file.  Hugging Face Spaces do not currently
+# support uploading an entire folder via the web interface, so the `hi3dgen`
+# source tree is bundled into a single `hi3dgen.zip` archive.  On startup we
+# extract this archive into the working directory if the `hi3dgen` package is
+# not already present.  This allows the rest of the code to `import hi3dgen` as
+# normal.
+# ---------------------------------------------------------------------------
+
+def _ensure_hi3dgen_available():
+    """Unpack hi3dgen.zip into the current directory if the hi3dgen package
+    is missing.  This function is idempotent and safe to call multiple times.
+    """
+    pkg_name = 'hi3dgen'
+    pkg_dir = os.path.join(os.path.dirname(__file__), pkg_name)
+    if os.path.isdir(pkg_dir):
+        return
+    archive_path = os.path.join(os.path.dirname(__file__), f"{pkg_name}.zip")
+    if os.path.isfile(archive_path):
+        try:
+            with zipfile.ZipFile(archive_path, 'r') as zf:
+                zf.extractall(os.path.dirname(__file__))
+        except Exception as e:
+            raise RuntimeError(f"Failed to extract {archive_path}: {e}")
+    else:
+        raise FileNotFoundError(
+            f"Required archive {archive_path} is missing. Make sure to upload the hi3dgen.zip file alongside app.py."
+        )
+
+# Make sure the hi3dgen package is available before importing it
+_ensure_hi3dgen_available()
+
+from hi3dgen.pipelines import Hi3DGenPipeline
+import trimesh
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+WEIGHTS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'weights')
+os.makedirs(TMP_DIR, exist_ok=True)
+os.makedirs(WEIGHTS_DIR, exist_ok=True)
+
+def cache_weights(weights_dir: str) -> dict:
+    import os
+    from huggingface_hub import snapshot_download
+
+    os.makedirs(weights_dir, exist_ok=True)
+    model_ids = [
+        "Stable-X/trellis-normal-v0-1",
+        "Stable-X/yoso-normal-v1-8-1",
+        "ZhengPeng7/BiRefNet",
+    ]
+    cached_paths = {}
+    for model_id in model_ids:
+        print(f"Caching weights for: {model_id}")
+        # Check if the model is already cached
+        local_path = os.path.join(weights_dir, model_id.split("/")[-1])
+        if os.path.exists(local_path):
+            print(f"Already cached at: {local_path}")
+            cached_paths[model_id] = local_path
+            continue
+        # Download the model and cache it
+        print(f"Downloading and caching model: {model_id}")
+        # Use snapshot_download to download the model
+        local_path = snapshot_download(repo_id=model_id, local_dir=os.path.join(weights_dir, model_id.split("/")[-1]), force_download=False)
+        cached_paths[model_id] = local_path
+        print(f"Cached at: {local_path}")
+
+    return cached_paths
+
+def preprocess_mesh(mesh_prompt):
+    print("Processing mesh")
+    trimesh_mesh = trimesh.load_mesh(mesh_prompt)
+    trimesh_mesh.export(mesh_prompt+'.glb')
+    return mesh_prompt+'.glb'
+
+def preprocess_image(image):
+    if image is None:
+        return None
+    image = hi3dgen_pipeline.preprocess_image(image, resolution=1024)
+    return image
+
+def generate_3d(image, seed=-1,  
+                ss_guidance_strength=3, ss_sampling_steps=50,
+                slat_guidance_strength=3, slat_sampling_steps=6,):
+    if image is None:
+        return None, None, None
+
+    if seed == -1:
+        seed = np.random.randint(0, MAX_SEED)
+    
+    image = hi3dgen_pipeline.preprocess_image(image, resolution=1024)
+    normal_image = normal_predictor(image, resolution=768, match_input_resolution=True, data_type='object')
+
+    outputs = hi3dgen_pipeline.run(
+        normal_image,
+        seed=seed,
+        formats=["mesh",],
+        preprocess_image=False,
+        sparse_structure_sampler_params={
+            "steps": ss_sampling_steps,
+            "cfg_strength": ss_guidance_strength,
+        },
+        slat_sampler_params={
+            "steps": slat_sampling_steps,
+            "cfg_strength": slat_guidance_strength,
+        },
+    )
+    generated_mesh = outputs['mesh'][0]
+    
+    # Save outputs
+    import datetime
+
+
+    output_id = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+    os.makedirs(os.path.join(TMP_DIR, output_id), exist_ok=True)
+    mesh_path = f"{TMP_DIR}/{output_id}/mesh.glb"
+    
+    # Export mesh
+    trimesh_mesh = generated_mesh.to_trimesh(transform_pose=True)
+
+    trimesh_mesh.export(mesh_path)
+
+    return normal_image, mesh_path, mesh_path
+
+def convert_mesh(mesh_path, export_format):
+    """Download the mesh in the selected format."""
+    if not mesh_path:
+        return None
+    
+    # Create a temporary file to store the mesh data
+    temp_file = tempfile.NamedTemporaryFile(suffix=f".{export_format}", delete=False)
+    temp_file_path = temp_file.name
+    
+    new_mesh_path = mesh_path.replace(".glb", f".{export_format}")
+    mesh = trimesh.load_mesh(mesh_path)
+    mesh.export(temp_file_path)  # Export to the temporary file
+    
+    return temp_file_path # Return the path to the temporary file
+
+# Create the Gradio interface with improved layout
+with gr.Blocks(css="footer {visibility: hidden}") as demo:
+    gr.Markdown(
+        """
+        <h1 style='text-align: center;'>Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging</h1>
+        <p style='text-align: center;'>
+            <strong>V0.1, Introduced By 
+            <a href="https://gaplab.cuhk.edu.cn/" target="_blank">GAP Lab</a> from CUHKSZ and 
+            <a href="https://www.nvsgames.cn/" target="_blank">Game-AIGC Team</a> from ByteDance</strong>
+        </p>
+        """
+    )
+    
+    with gr.Row():
+        gr.Markdown("""
+                    <p align="center">
+                    <a title="Website" href="https://stable-x.github.io/Hi3DGen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                        <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
+                    </a>
+                    <a title="arXiv" href="https://stable-x.github.io/Hi3DGen/hi3dgen_paper.pdf" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                        <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
+                    </a>
+                    <a title="Github" href="https://github.com/Stable-X/Hi3DGen" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                        <img src="https://img.shields.io/github/stars/Stable-X/Hi3DGen?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
+                    </a>
+                    <a title="Social" href="https://x.com/ychngji6" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                        <img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
+                    </a>
+                    </p>
+                    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Tabs():
+                
+                with gr.Tab("Single Image"):
+                    with gr.Row():
+                        image_prompt = gr.Image(label="Image Prompt", image_mode="RGBA", type="pil")
+                        normal_output = gr.Image(label="Normal Bridge", image_mode="RGBA", type="pil")
+                        
+                with gr.Tab("Multiple Images"):
+                    gr.Markdown("<div style='text-align: center; padding: 40px; font-size: 24px;'>Multiple Images functionality is coming soon!</div>")
+                        
+            with gr.Accordion("Advanced Settings", open=False):
+                seed = gr.Slider(-1, MAX_SEED, label="Seed", value=0, step=1)
+                gr.Markdown("#### Stage 1: Sparse Structure Generation")
+                with gr.Row():
+                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3, step=0.1)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=50, step=1)
+                gr.Markdown("#### Stage 2: Structured Latent Generation")
+                with gr.Row():
+                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
+                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=6, step=1)
+                    
+            with gr.Group():
+                with gr.Row():
+                    gen_shape_btn = gr.Button("Generate Shape", size="lg", variant="primary")
+                        
+        # Right column - Output
+        with gr.Column(scale=1):
+            with gr.Column():
+                model_output = gr.Model3D(label="3D Model Preview (Each model is approximately 40MB, may take around 1 minute to load)")
+            with gr.Column():
+                export_format = gr.Dropdown(
+                    choices=["obj", "glb", "ply", "stl"],
+                    value="glb",
+                    label="File Format"
+                )
+                download_btn = gr.DownloadButton(label="Export Mesh", interactive=False)
+
+    image_prompt.upload(
+        preprocess_image,
+        inputs=[image_prompt],
+        outputs=[image_prompt]
+    )
+    
+    gen_shape_btn.click(
+        generate_3d,
+        inputs=[
+            image_prompt, seed,  
+            ss_guidance_strength, ss_sampling_steps,
+            slat_guidance_strength, slat_sampling_steps
+        ],
+        outputs=[normal_output, model_output, download_btn]
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_btn],
+    )
+    
+    
+    def update_download_button(mesh_path, export_format):
+        if not mesh_path:
+            return gr.File.update(value=None, interactive=False)
+        
+        download_path = convert_mesh(mesh_path, export_format)
+        return download_path
+    
+    export_format.change(
+        update_download_button,
+        inputs=[model_output, export_format],
+        outputs=[download_btn]
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_btn],
+    )
+    
+    examples = None
+
+    gr.Markdown(
+        """
+        **Acknowledgments**: Hi3DGen is built on the shoulders of giants. We would like to express our gratitude to the open-source research community and the developers of these pioneering projects:
+        - **3D Modeling:** Our 3D Model is finetuned from the SOTA open-source 3D foundation model [Trellis](https://github.com/microsoft/TRELLIS) and we draw inspiration from the teams behind [Rodin](https://hyperhuman.deemos.com/rodin), [Tripo](https://www.tripo3d.ai/app/home), and [Dora](https://github.com/Seed3D/Dora).
+        - **Normal Estimation:** Our Normal Estimation Model builds on the leading normal estimation research such as [StableNormal](https://github.com/hugoycj/StableNormal) and [GenPercept](https://github.com/aim-uofa/GenPercept).
+        
+        **Your contributions and collaboration push the boundaries of 3D modeling!**
+        """
+    )
+
+if __name__ == "__main__":
+    # Download and cache the weights
+    cache_weights(WEIGHTS_DIR)
+
+    hi3dgen_pipeline = Hi3DGenPipeline.from_pretrained("weights/trellis-normal-v0-1")
+    hi3dgen_pipeline.cuda()
+
+    # Initialize normal predictor
+    try:
+        normal_predictor = torch.hub.load(os.path.join(torch.hub.get_dir(), 'hugoycj_StableNormal_main'), "StableNormal_turbo", yoso_version='yoso-normal-v1-8-1', source='local', local_cache_dir='./weights', pretrained=True)
+    except:
+        normal_predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal_turbo", trust_repo=True, yoso_version='yoso-normal-v1-8-1', local_cache_dir='./weights')    
+
+    # Launch the app
+    demo.launch(share=False, server_name="0.0.0.0")