🚀 Fresh deploy of Magic Articulate Enhanced MVP

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DEPLOYMENT.md

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+# 🚀 MagicArticulate MVP Deployment Guide
+
+## 部署到Hugging Face Space
+
+### 1. 准备工作
+
+确保你有以下账户和权限：
+- Hugging Face账户
+- Git配置
+- MagicArticulate模型权重（可选）
+
+### 2. 创建HF Space
+
+1. 访问 [Hugging Face Spaces](https://huggingface.co/spaces)
+2. 点击 "Create new Space"
+3. 配置Space信息：
+   - **Space name**: `magic-articulate-mvp` (或你喜欢的名称)
+   - **License**: MIT
+   - **SDK**: Gradio
+   - **Hardware**: ZeroGPU (免费)
+   - **Visibility**: Public
+
+### 3. 克隆和设置
+
+```bash
+# 克隆你的HF Space仓库
+git clone https://huggingface.co/spaces/YOUR_USERNAME/magic-articulate-mvp
+cd magic-articulate-mvp
+
+# 复制MVP文件
+cp -r /path/to/articulate-hub/mvp-space/* .
+
+# 设置MagicArticulate
+git clone https://github.com/Seed3D/MagicArticulate.git
+# 或者创建符号链接
+ln -s /path/to/MagicArticulate .
+```
+
+### 4. 配置文件
+
+确保以下文件正确配置：
+
+#### README.md (HF Space配置)
+```yaml
+---
+title: MagicArticulate MVP
+emoji: 🎯
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+hardware: zero-gpu
+---
+```
+
+#### requirements.txt
+所有必要的依赖已经列出，包括：
+- gradio==4.44.0
+- spaces[gpu]
+- torch==2.1.1
+- 其他依赖...
+
+### 5. 推送到HF Space
+
+```bash
+# 添加所有文件
+git add .
+
+# 提交更改
+git commit -m "🎯 Initial MagicArticulate MVP deployment
+
+Features:
+- 3D model upload and processing
+- Text-guided skeleton generation
+- ZeroGPU integration
+- Professional Gradio interface
+- Multiple output formats
+
+Ready for investor demonstrations!"
+
+# 推送到HF Space
+git push
+```
+
+### 6. 验证部署
+
+1. 访问你的HF Space URL
+2. 等待构建完成（通常5-10分钟）
+3. 测试基本功能：
+   - 文件上传
+   - 处理流程
+   - 结果下载
+
+### 7. 故障排除
+
+#### 常见问题：
+
+**构建失败**
+- 检查requirements.txt中的依赖版本
+- 确保所有文件都正确上传
+- 查看Space的构建日志
+
+**ZeroGPU不工作**
+- 确认README.md中有 `hardware: zero-gpu`
+- 检查`@spaces.GPU`装饰器的使用
+- 验证你的HF账户有ZeroGPU访问权限
+
+**MagicArticulate导入失败**
+- 确保MagicArticulate目录结构正确
+- 检查相对路径配置
+- 验证依赖是否完整
+
+**内存不足**
+- 减少batch_size
+- 优化模型加载
+- 使用fp16精度
+
+### 8. 性能优化
+
+#### 启动优化：
+```python
+# 在app.py中添加缓存
+@st.cache_resource
+def load_model():
+    return MagicArticulateWrapper()
+```
+
+#### 内存优化：
+- 使用torch.no_grad()
+- 及时清理临时文件
+- 限制并发请求数
+
+### 9. 监控和维护
+
+#### 关键指标：
+- 处理成功率
+- 平均处理时间
+- GPU利用率
+- 用户满意度
+
+#### 定期维护：
+- 更新依赖版本
+- 优化模型性能
+- 收集用户反馈
+- 修复已知问题
+
+### 10. 扩展计划
+
+#### 短期扩展：
+- 添加更多示例模型
+- 优化界面体验
+- 增加处理状态显示
+- 支持更多文件格式
+
+#### 长期集成：
+- 集成到主ArticulateHub平台
+- 添加用户管理
+- 实现批量处理
+- 集成Three.js可视化
+
+## 📊 部署检查清单
+
+- [ ] HF Space创建完成
+- [ ] 所有文件正确上传
+- [ ] README.md配置正确
+- [ ] requirements.txt包含所有依赖
+- [ ] MagicArticulate集成正确
+- [ ] ZeroGPU配置启用
+- [ ] 基本功能测试通过
+- [ ] 错误处理工作正常
+- [ ] 示例文件可用
+- [ ] 文档更新完成
+
+## 🎯 成功标准
+
+MVP部署成功的标准：
+1. ✅ Space可以正常访问
+2. ✅ 文件上传功能正常
+3. ✅ 处理流程无错误
+4. ✅ 结果可以下载
+5. ✅ 界面友好专业
+6. ✅ 处理时间合理（<2分钟）
+7. ✅ 适合投资人演示
+
+## 🔗 有用的链接
+
+- [Hugging Face Spaces文档](https://huggingface.co/docs/hub/spaces)
+- [ZeroGPU指南](https://huggingface.co/docs/hub/spaces-zerogpu)
+- [Gradio文档](https://gradio.app/docs)
+- [MagicArticulate项目](https://github.com/Seed3D/MagicArticulate)
+
+---
+
+**准备好向投资人展示你的AI驱动的3D模型骨骼生成技术了！** 🎉

README.md

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+---
+title: Magic Articulate Enhanced
+emoji: 🎯
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 5.36.2
+app_file: app.py
+pinned: false
+license: mit
+hardware: zero-gpu
+---
+
+# 🎯 Magic Articulate Enhanced
+
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Nomad2082/Magic-plus-1)
+[![ZeroGPU](https://img.shields.io/badge/ZeroGPU-Enabled-green)](https://huggingface.co/zero-gpu-explorer)
+
+## ✨ Enhanced Features
+
+🚀 **Revolutionary 3D Skeletal Rigging with AI**
+
+This enhanced version of MagicArticulate provides:
+
+### 🔥 Core Capabilities
+- **📁 Universal Model Support** - Upload ANY 3D model (OBJ, GLB, PLY, STL, FBX, DAE)
+- **🤖 AI-Powered Rigging** - Automatic skeletal structure generation
+- **🎨 Multi-Format Output** - Download as OBJ, TXT, or complete ZIP package
+- **👁️ Real-time 3D Preview** - Interactive Three.js visualization
+- **⚡ ZeroGPU Acceleration** - Free GPU processing in 30-120 seconds
+
+### 🆕 Enhanced Features
+- ✅ **User Upload Support** - No more demo-only limitations
+- ✅ **Advanced Model Validation** - Automatic repair and optimization  
+- ✅ **Professional Output Formats** - Industry-standard skeletal data
+- ✅ **Session Management** - Multi-user concurrent processing
+- ✅ **Intelligent Preprocessing** - Format conversion and mesh optimization
+
+## 🎯 Perfect for Investor Demonstrations
+
+This MVP showcases a complete AI-driven 3D workflow:
+
+1. **Upload** - Any 3D model from your device
+2. **Process** - AI generates optimal skeletal structure
+3. **Preview** - Real-time 3D visualization
+4. **Download** - Professional multi-format outputs
+
+## 🚀 Quick Start
+
+1. **Upload your 3D model** (supports most common formats)
+2. **Describe your requirements** (e.g., "human skeleton for animation")
+3. **Click Generate** and wait 30-120 seconds
+4. **Preview and Download** your rigged skeleton
+
+## 💡 Use Cases
+
+- **Game Development** - Character rigging automation
+- **Animation Studios** - Rapid skeleton prototyping  
+- **AR/VR Applications** - Real-time avatar creation
+- **3D Printing** - Articulated model preparation
+- **Research & Education** - Skeletal anatomy studies
+
+## 🔧 Technical Details
+
+### Supported Input Formats
+- **OBJ** - Wavefront object files
+- **GLB/GLTF** - 3D transmission format
+- **PLY** - Polygon file format
+- **STL** - Stereolithography format
+- **FBX** - Filmbox format
+- **DAE** - Collada format
+
+### Output Formats
+- **OBJ** - 3D geometric representation of the skeleton
+- **TXT** - Traditional rigging format for animation software
+- **ZIP** - Complete package with all formats and processing report
+
+### Processing Pipeline
+1. **Model Validation** - File format and mesh integrity checks
+2. **Automatic Repair** - Fix common mesh issues (holes, normals, duplicates)
+3. **Optimization** - Simplify complex models for faster processing
+4. **AI Generation** - Neural network skeletal structure prediction
+5. **Post-processing** - Joint optimization and bone hierarchy construction
+
+## 🎮 Example Use Cases
+
+### Game Character Rigging
+```
+Input: Character.fbx (game asset)
+Prompt: "humanoid skeleton for game animation with proper joint hierarchy"
+Output: Complete rigging data ready for Unity/Unreal
+```
+
+### Animal Animation
+```
+Input: Dog.obj (3D scan)  
+Prompt: "quadruped skeleton with spine and tail bones"
+Output: Anatomically correct animal rig
+```
+
+### Mechanical Rigging
+```
+Input: Robot.glb (CAD model)
+Prompt: "mechanical joints for robotic movement"
+Output: Engineering-ready joint structure
+```
+
+## 🧬 Powered by Advanced AI
+
+- **MagicArticulate Neural Network** - State-of-the-art skeletal generation
+- **Hugging Face ZeroGPU** - Free high-performance computing
+- **Advanced Preprocessing** - Intelligent model optimization
+- **Multi-User Architecture** - Concurrent processing support
+
+## 📊 Performance
+
+- **Processing Time**: 30-120 seconds (depending on model complexity)
+- **Max File Size**: 100MB
+- **Max Vertices**: 100,000 (auto-simplified if needed)
+- **Concurrent Users**: Multi-session support
+- **Accuracy**: State-of-the-art AI skeletal prediction
+
+## Citation
+
+If you use this work, please cite:
+```
+@article{magicarticulate2024,
+  title={MagicArticulate: Automatic Skeletal Rigging for 3D Models},
+  author={ByteDance Research},
+  year={2024}
+}
+```

app.py

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+"""
+MagicArticulate MVP - 增强版Gradio应用
+支持多格式文件下载和预览
+"""
+
+import os
+import sys
+import time
+import logging
+import tempfile
+import traceback
+from pathlib import Path
+from typing import Optional, Dict, Any, List, Tuple
+import shutil
+import zipfile
+
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+
+# 添加src目录到路径
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
+from enhanced_magic_wrapper import EnhancedMagicWrapper
+from config import get_config, DEMO_PROMPTS, EXAMPLE_MODELS
+from src.utils import (
+    validate_file, get_model_info, cleanup_temp_files, 
+    format_processing_time, get_prompt_suggestions,
+    create_processing_status, estimate_processing_time,
+    generate_download_filename, safe_json_serialize
+)
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# 获取配置
+config = get_config()
+
+# 全局变量
+magic_wrapper = None
+processing_status = {}
+session_results = {}  # 存储处理结果
+
+def initialize_app():
+    """初始化应用"""
+    global magic_wrapper
+    
+    try:
+        logger.info("🚀 Initializing MagicArticulate MVP LFG...")
+        logger.info(f"🔍 Current working directory: {os.getcwd()}")
+        logger.info(f"🔍 Script directory: {os.path.dirname(__file__)}")
+        
+        # 检查关键目录结构
+        directories = ['src', 'utils', 'skeleton_models', 'magic_articulate_plus', 'third_party']
+        for dir_name in directories:
+            dir_path = os.path.join(os.getcwd(), dir_name)
+            exists = os.path.exists(dir_path)
+            logger.info(f"🔍 Directory {dir_name}: exists={exists}")
+            if exists and os.path.isdir(dir_path):
+                try:
+                    contents = os.listdir(dir_path)[:5]  # 只显示前5个文件
+                    logger.info(f"🔍   Contents (first 5): {contents}")
+                except Exception as e:
+                    logger.warning(f"🔍   Could not list contents: {e}")
+        
+        # 首先下载所需的模型文件
+        try:
+            logger.info("📥 开始下载模型文件...")
+            from download_models import download_models
+            download_models()
+        except Exception as e:
+            logger.warning(f"⚠️ 模型下载过程中出现问题: {e}")
+            import traceback
+            logger.warning(f"⚠️ Download traceback: {traceback.format_exc()}")
+        
+        # 创建增强版包装器实例（支持真实3D模型处理）
+        logger.info("🔧 Creating EnhancedMagicWrapper instance...")
+        magic_wrapper = EnhancedMagicWrapper()
+        
+        # 初始化包装器
+        logger.info("🔧 Initializing wrapper...")
+        if magic_wrapper.initialize():
+            logger.info("✅ MagicArticulate MVP initialized successfully")
+            return True
+        else:
+            logger.error("❌ Failed to initialize MagicArticulate wrapper")
+            return False
+            
+    except Exception as e:
+        logger.error(f"💥 App initialization failed: {str(e)}")
+        logger.error(traceback.format_exc())
+        return False
+
+def create_download_package(output_files: Dict[str, str], session_id: str) -> str:
+    """
+    创建包含所有输出文件的ZIP包
+    
+    Args:
+        output_files: 输出文件路径字典
+        session_id: 会话ID
+        
+    Returns:
+        ZIP文件路径
+    """
+    try:
+        # 创建临时目录
+        temp_dir = Path(tempfile.mkdtemp())
+        zip_path = temp_dir / f"skeleton_results_{session_id}.zip"
+        
+        # 创建ZIP文件
+        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+            for file_type, file_path in output_files.items():
+                if os.path.exists(file_path):
+                    # 使用描述性的文件名
+                    if 'skeleton_json' in file_type:
+                        arcname = "skeleton_data.json"
+                    elif 'skeleton_obj' in file_type:
+                        arcname = "skeleton_model.obj"
+                    elif 'skeleton_txt' in file_type:
+                        arcname = "skeleton_rig.txt"
+                    elif 'processed_mesh' in file_type:
+                        arcname = "processed_mesh.obj"
+                    elif 'report' in file_type:
+                        arcname = "processing_report.json"
+                    else:
+                        arcname = os.path.basename(file_path)
+                    
+                    zipf.write(file_path, arcname)
+                    logger.info(f"Added {arcname} to ZIP")
+        
+        logger.info(f"Created download package: {zip_path}")
+        return str(zip_path)
+        
+    except Exception as e:
+        logger.error(f"Failed to create download package: {str(e)}")
+        return None
+
+@spaces.GPU(duration=120)
+def process_3d_model_gpu(
+    model_file: gr.File,
+    prompt: str,
+    confidence_threshold: float,
+    generate_preview: bool
+) -> Tuple[str, str, Any, Any, Any, Any, str, str]:
+    """
+    GPU处理函数 - 使用ZeroGPU
+    返回多个文件供下载
+    
+    Returns:
+        (状态, 文本展示, OBJ下载, TXT下载, ZIP下载, 处理信息, 错误信息, 骨骼数据)
+    """
+    global magic_wrapper, session_results
+    
+    start_time = time.time()
+    session_id = f"session_{int(start_time)}"
+    
+    try:
+        logger.info(f"🔄 Starting GPU processing for session: {session_id}")
+        
+        # 验证输入
+        if model_file is None:
+            return "❌ 错误", "", None, None, None, None, "", "请上传3D模型文件"
+        
+        if not prompt.strip():
+            prompt = DEMO_PROMPTS['generic']
+            logger.info(f"Using default prompt: {prompt}")
+        
+        # 验证文件
+        file_path = model_file.name
+        is_valid, error_msg = validate_file(file_path, config['file_limits']['max_size_mb'])
+        if not is_valid:
+            return "❌ 错误", "", None, None, None, None, "", f"文件验证失败: {error_msg}"
+        
+        # 获取模型信息
+        model_info = get_model_info(file_path)
+        logger.info(f"📊 Model info: {model_info}")
+        
+        # 估算处理时间
+        estimated_time = estimate_processing_time(model_info)
+        logger.info(f"⏱️ Estimated processing time: {estimated_time:.1f}s")
+        
+        # 更新处理状态
+        processing_status[session_id] = create_processing_status(
+            "preparing", 0.1, "准备处理3D模型..."
+        )
+        
+        # 调用MagicArticulate处理
+        if magic_wrapper is None:
+            logger.error("MagicArticulate wrapper not initialized")
+            return "❌ 错误", "", None, None, None, None, "", "AI模型未初始化"
+        
+        processing_status[session_id] = create_processing_status(
+            "processing", 0.3, "正在生成骨骼结构..."
+        )
+        
+        # 执行处理
+        result = magic_wrapper.process_3d_model(
+            model_file_path=file_path,
+            prompt=prompt,
+            confidence_threshold=confidence_threshold,
+            generate_preview=generate_preview
+        )
+        
+        processing_status[session_id] = create_processing_status(
+            "finalizing", 0.9, "正在准备输出文件..."
+        )
+        
+        # 处理结果
+        if not result['success']:
+            error_msg = result.get('error', 'Unknown error')
+            logger.error(f"Processing failed: {error_msg}")
+            return "❌ 处理失败", "", None, None, None, None, "", error_msg
+        
+        # 保存结果到会话
+        session_results[session_id] = result
+        
+        # 准备输出数据
+        skeleton_data = result['skeleton_data']
+        output_files = result['output_files']
+        processing_info = result['processing_info']
+        
+        # 格式化骨骼数据为文本显示
+        skeleton_json = f"""骨骼结构数据预览
+===================
+
+关节数量: {skeleton_data.get('joint_count', 0)}
+骨骼数量: {skeleton_data.get('bone_count', 0)}
+根节点索引: {skeleton_data.get('root_index', 0)}
+
+关节坐标 (前10个):
+{str(skeleton_data.get('joints', [])[:10])}
+
+骨骼连接 (前10个):
+{str(skeleton_data.get('bones', [])[:10])}
+
+用户提示: {skeleton_data.get('user_prompt', 'N/A')}
+"""
+        
+        # 准备各个文件供下载
+        obj_file = output_files.get('skeleton_obj', None)
+        txt_file = output_files.get('skeleton_txt', None)
+        
+        # 创建ZIP包含所有文件
+        zip_file = create_download_package(output_files, session_id)
+        
+        # 处理时间
+        processing_time = time.time() - start_time
+        
+        # 准备处理信息
+        info_text = f"""
+## 处理完成! ✅
+
+### 📊 处理统计
+- **输入文件**: {processing_info.get('input_file', 'Unknown')}
+- **处理时间**: {format_processing_time(processing_time)}
+- **提示词**: {processing_info.get('prompt', 'None')}
+
+### 🦴 骨骼数据
+- **关节数量**: {processing_info.get('joint_count', 0)}
+- **骨骼数量**: {processing_info.get('bone_count', 0)}
+- **根节点索引**: {skeleton_data.get('root_index', 0)}
+
+### 📁 可下载文件
+1. **骨骼模型 (OBJ)** - 3D骨骼的几何表示，可在3D软件中查看
+2. **绑定数据 (TXT)** - 传统的骨骼绑定格式，适合导入到动画软件
+3. **完整包 (ZIP)** - 包含所有输出文件的压缩包
+
+### 💡 使用建议
+- OBJ格式可以直接在Blender、Maya等3D软件中查看
+- TXT格式符合传统骨骼绑定标准，便于集成到现有工作流程
+- ZIP包含所有文件和处理报告，方便归档和分享
+        """
+        
+        processing_status[session_id] = create_processing_status(
+            "completed", 1.0, "处理完成!"
+        )
+        
+        logger.info(f"✅ Processing completed successfully in {processing_time:.1f}s")
+        
+        return (
+            "✅ 处理完成",
+            skeleton_json,
+            obj_file,
+            txt_file,
+            zip_file,
+            info_text,
+            "",
+            skeleton_data  # 添加原始skeleton_data用于3D预览
+        )
+        
+    except Exception as e:
+        processing_time = time.time() - start_time
+        error_msg = f"处理过程中发生错误: {str(e)}"
+        logger.error(f"💥 Processing error: {error_msg}")
+        logger.error(traceback.format_exc())
+        
+        processing_status[session_id] = create_processing_status(
+            "error", 0.0, error_msg
+        )
+        
+        return (
+            "❌ 处理失败",
+            "",
+            None,
+            None,
+            None,
+            f"处理时间: {format_processing_time(processing_time)}",
+            error_msg,
+            None  # 空的skeleton_data
+        )
+
+def create_visualization_html(skeleton_data: Dict[str, Any]) -> str:
+    """
+    创建骨骼可视化的HTML
+    使用Three.js进行简单的3D展示
+    """
+    joints = skeleton_data.get('joints', [])
+    bones = skeleton_data.get('bones', [])
+    
+    html_content = f"""
+    <div id="skeleton-viewer" style="width: 100%; height: 400px; border: 1px solid #ddd;">
+        <canvas id="three-canvas" style="width: 100%; height: 100%;"></canvas>
+    </div>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
+    <script>
+        // 简单的Three.js骨骼可视化
+        const scene = new THREE.Scene();
+        scene.background = new THREE.Color(0xf0f0f0);
+        
+        const camera = new THREE.PerspectiveCamera(75, 1, 0.1, 1000);
+        camera.position.set(2, 2, 2);
+        camera.lookAt(0, 0, 0);
+        
+        const renderer = new THREE.WebGLRenderer({{canvas: document.getElementById('three-canvas')}});
+        renderer.setSize(400, 400);
+        
+        // 添加光源
+        const light = new THREE.DirectionalLight(0xffffff, 1);
+        light.position.set(1, 1, 1);
+        scene.add(light);
+        
+        // 添加网格
+        const gridHelper = new THREE.GridHelper(4, 10);
+        scene.add(gridHelper);
+        
+        // 骨骼数据
+        const joints = {json.dumps(joints)};
+        const bones = {json.dumps(bones)};
+        
+        // 创建关节球体
+        joints.forEach((joint, index) => {{
+            const geometry = new THREE.SphereGeometry(0.05);
+            const material = new THREE.MeshPhongMaterial({{color: 0xff0000}});
+            const sphere = new THREE.Mesh(geometry, material);
+            sphere.position.set(joint[0], joint[1], joint[2]);
+            scene.add(sphere);
+        }});
+        
+        // 创建骨骼线条
+        bones.forEach(bone => {{
+            const start = joints[bone[0]];
+            const end = joints[bone[1]];
+            
+            const points = [];
+            points.push(new THREE.Vector3(start[0], start[1], start[2]));
+            points.push(new THREE.Vector3(end[0], end[1], end[2]));
+            
+            const geometry = new THREE.BufferGeometry().setFromPoints(points);
+            const material = new THREE.LineBasicMaterial({{color: 0x0000ff}});
+            const line = new THREE.Line(geometry, material);
+            scene.add(line);
+        }});
+        
+        // 动画循环
+        function animate() {{
+            requestAnimationFrame(animate);
+            scene.rotation.y += 0.01;
+            renderer.render(scene, camera);
+        }}
+        animate();
+    </script>
+    """
+    
+    return html_content
+
+def create_demo_interface():
+    """创建增强版Gradio界面"""
+    
+    # 自定义CSS
+    custom_css = """
+    .gradio-container {
+        max-width: 1400px;
+        margin: 0 auto;
+    }
+    
+    .download-section {
+        border: 2px solid #e0e0e0;
+        border-radius: 10px;
+        padding: 20px;
+        margin: 10px 0;
+        background-color: #f9f9f9;
+    }
+    
+    .status-box {
+        border: 1px solid #ddd;
+        border-radius: 8px;
+        padding: 15px;
+        margin: 10px 0;
+        background-color: #f8f9fa;
+    }
+    
+    .success-status {
+        border-color: #28a745;
+        background-color: #d4edda;
+    }
+    
+    .error-status {
+        border-color: #dc3545;
+        background-color: #f8d7da;
+    }
+    
+    .info-panel {
+        font-family: monospace;
+        font-size: 14px;
+        line-height: 1.4;
+    }
+    
+    .file-download-btn {
+        margin: 5px;
+        min-width: 200px;
+    }
+    """
+    
+    # 创建界面
+    with gr.Blocks(
+        title=config['ui']['title'] + " Enhanced",
+        theme=gr.themes.Soft(),
+        css=custom_css
+    ) as demo:
+        
+        # 标题和描述
+        gr.Markdown(f"""
+        # {config['ui']['title']} - 增强版
+        
+        {config['ui']['description']}
+        
+        ### ✨ 增强功能
+        - 📁 **多格式下载** - OBJ, TXT, ZIP
+        - 👁️ **骨骼预览** - 3D可视化展示
+        - 📊 **详细统计** - 完整的处理信息
+        - 🚀 **批量下载** - 一键下载所有文件
+        """)
+        
+        # 主界面
+        with gr.Row():
+            # 左侧 - 输入
+            with gr.Column(scale=1):
+                gr.Markdown("### 📤 输入设置")
+                
+                # 文件上传
+                model_file = gr.File(
+                    label="上传3D模型",
+                    file_types=['.obj', '.glb', '.ply', '.stl'],
+                    file_count="single"
+                )
+                
+                # 提示词输入
+                prompt_input = gr.Textbox(
+                    label="提示词",
+                    placeholder="描述你想要的骨骼类型，例如：realistic human skeleton for animation",
+                    lines=3,
+                    value=DEMO_PROMPTS['generic']
+                )
+                
+                # 提示词建议
+                with gr.Accordion("💡 提示词建议", open=False):
+                    for key, prompt in DEMO_PROMPTS.items():
+                        gr.Button(
+                            f"{key.title()}: {prompt}",
+                            size="sm"
+                        ).click(
+                            lambda p=prompt: p,
+                            outputs=prompt_input
+                        )
+                
+                # 高级选项
+                with gr.Accordion("⚙️ 高级选项", open=False):
+                    confidence_threshold = gr.Slider(
+                        label="置信度阈值",
+                        minimum=0.1,
+                        maximum=1.0,
+                        value=0.8,
+                        step=0.1
+                    )
+                    
+                    generate_preview = gr.Checkbox(
+                        label="生成预览图",
+                        value=True
+                    )
+                
+                # 处理按钮
+                process_btn = gr.Button(
+                    "🎯 生成骨骼",
+                    variant="primary",
+                    size="lg"
+                )
+            
+            # 右侧 - 输出
+            with gr.Column(scale=2):
+                gr.Markdown("### 📥 处理结果")
+                
+                # 状态显示
+                status_text = gr.Textbox(
+                    label="处理状态",
+                    value="等待处理...",
+                    interactive=False
+                )
+                
+                # 标签页组织输出
+                with gr.Tabs():
+                    # 数据展示标签
+                    with gr.TabItem("📊 骨骼数据"):
+                        skeleton_data_json = gr.Textbox(
+                            label="骨骼数据预览",
+                            lines=15,
+                            interactive=False,
+                            show_copy_button=True
+                        )
+                    
+                    # 3D预览标签
+                    with gr.TabItem("👁️ 3D预览"):
+                        skeleton_preview = gr.HTML(
+                            label="骨骼可视化",
+                            value="<p>等待处理...</p>"
+                        )
+                    
+                    # 下载标签
+                    with gr.TabItem("📁 文件下载"):
+                        gr.Markdown("### 下载骨骼文件")
+                        
+                        with gr.Row():
+                            download_obj = gr.File(
+                                label="🎨 OBJ格式", 
+                                visible=True
+                            )
+                            download_txt = gr.File(
+                                label="📝 TXT格式",
+                                visible=True
+                            )
+                        
+                        with gr.Row():
+                            download_zip = gr.File(
+                                label="📦 完整包(ZIP)",
+                                visible=True
+                            )
+                    
+                    # 处理信息标签
+                    with gr.TabItem("ℹ️ 处理信息"):
+                        processing_info = gr.Markdown(
+                            value="等待处理..."
+                        )
+                
+                # 错误信息（通常隐藏）
+                error_info = gr.Textbox(
+                    label="错误信息",
+                    visible=False,
+                    interactive=False
+                )
+        
+        # 示例模型
+        with gr.Accordion("📂 示例模型", open=False):
+            gr.Examples(
+                examples=[
+                    ["examples/boy.obj", "realistic human skeleton for animation"],
+                    ["examples/dog.obj", "four-legged animal with spine and tail"],
+                    ["examples/bird.obj", "bird skeleton with wing bones"],
+                ],
+                inputs=[model_file, prompt_input],
+                label="点击加载示例"
+            )
+        
+        # 使用说明
+        with gr.Accordion("📖 使用说明", open=False):
+            gr.Markdown("""
+            ## 🎯 如何使用
+            
+            1. **上传模型** - 支持OBJ, GLB, PLY, STL格式
+            2. **输入提示词** - 描述期望的骨骼类型
+            3. **点击生成** - 等待30-120秒
+            4. **查看结果** - 在不同标签页查看数据、预览和下载
+            
+            ## 📁 输出文件说明
+            
+            - **OBJ** - 可在3D软件中查看的骨骼模型
+            - **TXT** - 传统骨骼绑定格式
+            - **ZIP** - 包含所有文件的压缩包
+            
+            ## 💡 提示
+            
+            - 模型应该是封闭的网格以获得最佳效果
+            - 复杂模型可能需要更长处理时间
+            - 使用具体的提示词可以获得更好的结果
+            """)
+        
+        # 事件绑定
+        def process_and_update_ui(model_file, prompt, confidence, preview):
+            # 处理模型
+            status, json_data, obj_file, txt_file, zip_file, info, error, skeleton_data = process_3d_model_gpu(
+                model_file, prompt, confidence, preview
+            )
+            
+            # 生成3D预览
+            preview_html = "<p>暂无预览</p>"
+            if status == "✅ 处理完成" and skeleton_data:
+                try:
+                    preview_html = create_visualization_html(skeleton_data)
+                except Exception as e:
+                    preview_html = f"<p>预览生成失败: {str(e)}</p>"
+            
+            # 更新可见性
+            error_visible = status.startswith("❌")
+            
+            return (
+                status,           # 状态
+                json_data,        # JSON展示
+                obj_file,         # OBJ下载
+                txt_file,         # TXT下载
+                zip_file,         # ZIP下载
+                preview_html,     # 3D预览
+                info,            # 处理信息
+                error,           # 错误信息
+                gr.update(visible=error_visible)  # 错误框可见性
+            )
+        
+        # 绑定处理按钮
+        process_btn.click(
+            fn=process_and_update_ui,
+            inputs=[
+                model_file,
+                prompt_input,
+                confidence_threshold,
+                generate_preview
+            ],
+            outputs=[
+                status_text,
+                skeleton_data_json,
+                download_obj,
+                download_txt,
+                download_zip,
+                skeleton_preview,
+                processing_info,
+                error_info,
+                error_info  # 控制可见性
+            ]
+        )
+        
+        # 页脚
+        gr.Markdown("""
+        ---
+        
+        ## 🔗 相关链接
+        - [MagicArticulate Paper](https://github.com/Seed3D/MagicArticulate)
+        - [ArticulateHub Project](https://github.com/your-repo)
+        - [Hugging Face Spaces](https://huggingface.co/spaces)
+        
+        **Made with ❤️ using Gradio and ZeroGPU**
+        """)
+    
+    return demo
+
+def main():
+    """主函数"""
+    try:
+        logger.info("🚀 Starting Enhanced MagicArticulate MVP...")
+        
+        # 初始化应用
+        if not initialize_app():
+            logger.error("❌ Failed to initialize app")
+            return
+        
+        # 创建界面
+        demo = create_demo_interface()
+        
+        # 启动应用
+        logger.info("🌟 Launching Enhanced Gradio interface...")
+        demo.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            show_api=False,
+            share=False,
+            debug=False
+        )
+        
+    except Exception as e:
+        logger.error(f"💥 Main function failed: {str(e)}")
+        logger.error(traceback.format_exc())
+
+if __name__ == "__main__":
+    main()

data_utils/README.md

@@ -0,0 +1,43 @@
+## Preprocessed data
+We provide the preprocessed data that saved in NPZ files, which contain the following information:
+```
+'vertices', 'faces', 'normals', 'joints', 'bones', 'root_index', 'uuid', 'pc_w_norm', 'joint_names', 'skinning_weights_value', 'skinning_weights_rows', 'skinning_weights_cols', 'skinning_weights_shape'
+```
+You can check `read_npz.py` for how to read the NPZ files and `save_npz.py` for how we save them.
+
+Before saving them into NPZ files, we extract mesh(.obj) and rig(.txt) from downloaded 3D models from Objaverse-XL using Blender. The rig file follows the format in [RigNet](https://github.com/zhan-xu/RigNet), which includes the following entries:
+```
+joints [joint_name] [x] [y] [z]
+root [root_joint_name]
+skin [vertex_index] [joints_name1] [skinning_weight1] [joints_name2] [skinning_weight2] ...
+hier [parent_joint_name] [child_joint_name]
+```
+For an example, please see `examples/0a59c5ffa4a1476bac6d540b79947f31.txt`.
+
+If you want to convert NPZ file back to OBJ and TXT files, we give an example by running:
+```
+python convert_npz_to_mesh_rig.py
+```
+
+## Visualization
+We provide a method for visualizing 3D models with skeleton using [Pyrender](https://github.com/mmatl/pyrender), modified from [Lab4D](https://github.com/lab4d-org/lab4d/tree/ppr/). This visualization also serves as input to the VLM for skeleton quality rating. Make sure you have installed the following packages before running visualization:
+```
+pip install trimesh opencv-python pyrender
+```
+
+We provide an example to demonstrate the process. For this example, we prepare an OBJ file along with a TXT file containing rigging information. Then, run:
+```
+python render_data.py
+```
+You will obtain the following outputs:
+
+<p align="center">
+  <img width="80%" src="examples/0a59c5ffa4a1476bac6d540b79947f31_render_results.png"/>
+</p>
+
+### Reading rig and mesh from GLBs
+We provide the script we use for reading rig (.txt) and mesh (.obj) from glb files. You can run:
+```
+python read_rig_mesh_from_glb.py
+```
+Remember to download Blender (we use 4.2.0) and also bpy in your conda environment.

data_utils/__init__.py

@@ -0,0 +1,8 @@
+"""
+MagicArticulate Data Utils Package
+包含数据处理、渲染和加载的工具函数
+"""
+
+from .pyrender_wrapper import PyRenderWrapper
+
+__all__ = ['PyRenderWrapper']

data_utils/clean_skin_in_npz.py

@@ -0,0 +1,95 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import numpy as np
+import scipy.sparse as sp
+import os
+
+def check_and_clean_skinning_weights(file_path, output_path, tolerance=0.1):
+    """
+    Check if all rows in pc_skinning_weights sum to 1 for each item in the NPZ file.
+    Remove invalid items and save a cleaned version.
+    
+    Args:
+        file_path: Path to the input NPZ file
+        output_path: Path for the cleaned NPZ file
+        tolerance: Tolerance for floating point comparison
+        
+    Returns:
+        tuple: (cleaned_data_list, removed_indices)
+    """
+    data_list = np.load(file_path, allow_pickle=True)['arr_0']
+    
+    invalid_indices = []
+    valid_data_list = []
+    
+    for idx, data in enumerate(data_list):
+        is_valid = True
+
+        weights_data = data['skinning_weights_value']
+        weights_row = data['skinning_weights_row']
+        weights_col = data['skinning_weights_col']
+        weights_shape = data['skinning_weights_shape']
+            
+        skinning_sparse = sp.coo_matrix(
+            (weights_data, (weights_row, weights_col)), 
+            shape=weights_shape
+        )
+        
+        skinning_csr = skinning_sparse.tocsr()
+        row_sums = np.array(skinning_csr.sum(axis=1)).flatten()
+
+        invalid_rows = np.where(np.abs(row_sums - 1.0) > tolerance)[0]
+        
+        if len(invalid_rows) > 0:
+            min_sum = np.min(row_sums)
+            max_sum = np.max(row_sums)
+            invalid_indices.append((data['uuid'], f"{len(invalid_rows)} rows, range: [{min_sum:.6f}, {max_sum:.6f}]"))
+            is_valid = False
+    
+        if is_valid:
+            valid_data_list.append(data)
+    
+    # Save the cleaned data
+    if valid_data_list:
+        np.savez_compressed(output_path, valid_data_list, allow_pickle=True) 
+        print(f"Saved {len(valid_data_list)} valid items to {output_path}")
+
+    return valid_data_list, invalid_indices
+
+def main():
+    # File paths
+    file_path = "articulation_xlv2_train.npz"  # "articulation_xlv2_test.npz"
+    log_file = "invalid_skinning_weights_intrain.txt" # "invalid_skinning_weights_intest.txt"
+    output_path = "articulation_xlv2_train_updated.npz"  # "articulation_xlv2_test_updated.npz"
+
+    # Clean the data
+    valid_data, invalid_indices = check_and_clean_skinning_weights(file_path, output_path)
+    
+    # Log the results
+    with open(log_file, "w") as f:
+        f.write(f"Original file: {file_path}\n")
+        f.write(f"Cleaned file: {output_path}\n")
+        f.write(f"Total items: {len(np.load(file_path, allow_pickle=True)['arr_0'])}\n")
+        f.write(f"Valid items: {len(valid_data)}\n")
+        f.write(f"Removed items: {len(invalid_indices)}\n\n")
+        
+        if invalid_indices:
+            f.write("Details of removed items:\n")
+            for idx, details in invalid_indices:
+                f.write(f"  Index {idx}: {details}\n")
+    
+    print(f"Cleaning complete. Results written to {log_file}")
+
+if __name__ == "__main__":
+    main()

data_utils/convert_npz_to_mesh_rig.py

@@ -0,0 +1,107 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""
+You can convert npz file back to obj(mesh) and txt(rig) files using this python script.
+"""
+import os
+import numpy as np
+import scipy.sparse as sp
+
+def export_obj(vertices, faces, normals, output_path):
+    with open(output_path, 'w') as f:
+        for v in vertices:
+            f.write(f"v {v[0]} {v[1]} {v[2]}\n")
+        for n in normals:
+            f.write(f"vn {n[0]} {n[1]} {n[2]}\n")
+        for i, face in enumerate(faces):
+            # OBJ format is 1-based, so we add 1 to all indices
+            f.write(f"f {face[0]+1}//{face[0]+1} {face[1]+1}//{face[1]+1} {face[2]+1}//{face[2]+1}\n")
+
+def export_rig_txt(joints, bones, root_index, joint_names, skinning_weights, output_path):
+    """
+    joints [joint_name] [x] [y] [z]
+    root [root_joint_name]
+    skin [vertex_index] [joint_name1] [weight1] [joint_name2] [weight2] ...
+    hier [parent_joint_name] [child_joint_name]
+    """
+    n_joints = len(joints)
+    n_verts = skinning_weights.shape[0]  # (n_vertex, n_joints)
+
+    with open(output_path, 'w') as f:
+        # 1) joints
+        for i in range(n_joints):
+            x, y, z = joints[i]
+            jn = joint_names[i]
+            f.write(f"joints {jn} {x} {y} {z}\n")
+
+        # 2) root
+        root_name = joint_names[root_index]
+        f.write(f"root {root_name}\n")
+
+        # 3) skin 
+        for vidx in range(n_verts):
+            row_weights = skinning_weights[vidx]
+            non_zero_indices = np.where(row_weights != 0)[0]
+            if len(non_zero_indices) == 0:
+                continue 
+            
+            line_parts = [f"skin {vidx}"]  # vertex_idx
+            for jidx in non_zero_indices:
+                w = row_weights[jidx]
+                jn = joint_names[jidx]
+                line_parts.append(jn)
+                line_parts.append(str(w))
+            
+            f.write(" ".join(line_parts) + "\n")
+
+        # 4) hier
+        for p_idx, c_idx in bones:
+            p_name = joint_names[p_idx]
+            c_name = joint_names[c_idx]
+            f.write(f"hier {p_name} {c_name}\n")
+
+if __name__ == "__main__":
+    
+    data = np.load('articulation_xlv2_test.npz', allow_pickle=True)
+    data_list = data['arr_0'] 
+
+    print(f"Loaded {len(data_list)} data entries")
+
+    model_data = data_list[0]
+    print("Data keys:", model_data.keys())
+    # 'vertices', 'faces', 'normals', 'joints', 'bones', 'root_index', 'uuid', 'joint_names',
+    # 'skinning_weights_value', 'skinning_weights_row', 'skinning_weights_col', 'skinning_weights_shape'
+
+    vertices = model_data['vertices']          # (n_vertex, 3)
+    faces = model_data['faces']                # (n_faces, 3)
+    normals = model_data['normals']            # (n_vertex, 3)
+    joints = model_data['joints']              # (n_joints, 3)
+    bones = model_data['bones']                # (n_bones, 2)
+    root_index = model_data['root_index']      # int
+    joint_names = model_data['joint_names']    # list of str
+    uuid_str = model_data['uuid']             
+
+    skin_val = model_data['skinning_weights_value']
+    skin_row = model_data['skinning_weights_row']
+    skin_col = model_data['skinning_weights_col']
+    skin_shape = model_data['skinning_weights_shape']
+    skin_sparse = sp.coo_matrix((skin_val, (skin_row, skin_col)), shape=skin_shape)
+    skinning_weights = skin_sparse.toarray()  # (n_vertex, n_joints)
+
+    obj_path = f"{uuid_str}.obj" 
+    export_obj(vertices, faces, normals, obj_path)
+    rig_txt_path = f"{uuid_str}.txt"
+    export_rig_txt(joints, bones, root_index, joint_names, skinning_weights, rig_txt_path)
+
+    print("Done!")

data_utils/data_loader.py

@@ -0,0 +1,121 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import json
+import glob
+import numpy as np
+import trimesh
+
+class DataLoader:
+    def __init__(self):
+        self.joint_name_to_idx = {}
+
+    def load_rig_data(self, rig_path):
+        joints = []
+        joints_names = []
+        bones = []
+
+        with open(rig_path, 'r') as f:
+            for line in f:
+                parts = line.strip().split()
+                if parts[0] == 'joints':
+                    joint_name = parts[1]
+                    joint_pos = [float(parts[2]), float(parts[3]), float(parts[4])]
+                    self.joint_name_to_idx[joint_name] = len(joints)
+                    joints.append(joint_pos)
+                    joints_names.append(joint_name)
+                elif parts[0] == 'root':
+                    self.root_name = parts[1]
+                elif parts[0] == 'hier':
+                    parent_joint = self.joint_name_to_idx[parts[1]]
+                    child_joint = self.joint_name_to_idx[parts[2]]
+                    bones.append([parent_joint, child_joint])
+
+        self.joints = np.array(joints)
+        self.bones = np.array(bones)
+        self.joints_names = joints_names
+        self.root_idx = None
+        if self.root_name is not None:
+            self.root_idx = self.joint_name_to_idx[self.root_name]
+
+    def load_mesh(self, mesh_path):
+        mesh = trimesh.load(mesh_path, process=False)
+        mesh.visual.vertex_colors[:, 3] = 100  # set transparency
+        self.mesh = mesh
+        
+        # Compute the centroid normal of the mesh
+        v = self.mesh.vertices
+        xmin, ymin, zmin = v.min(axis=0)
+        xmax, ymax, zmax = v.max(axis=0)
+        self.bbox_center = np.array([(xmax + xmin)/2, (ymax + ymin)/2, (zmax + zmin)/2])
+        self.bbox_size = np.array([xmax - xmin, ymax - ymin, zmax - zmin])
+        self.bbox_scale = max(xmax - xmin, ymax - ymin, zmax - zmin)
+
+        normal = mesh.center_mass - self.bbox_center
+        normal = normal / (np.linalg.norm(normal)+1e-5)
+
+        # Choose axis order based on normal direction
+        if abs(normal[1]) > abs(normal[2]):  # if Y component is dominant
+            self.axis_order = [0, 1, 2]  # swapping Y and Z
+        else:
+            self.axis_order =[0, 2, 1]  # keep default order
+
+        self.mesh.vertices = self.mesh.vertices[:, self.axis_order]
+        self.joints = self.joints[:, self.axis_order]
+        self.normalize_coordinates()
+
+    def normalize_coordinates(self):
+        
+        # Compute scale and offset
+        scale = 1.0 / (self.bbox_scale+1e-5)
+        offset = -self.bbox_center
+
+        self.mesh.vertices = (self.mesh.vertices + offset) * scale
+        self.joints = (self.joints + offset) * scale
+        
+        # Calculate appropriate radii based on the mean size
+        self.joint_radius = 0.01 
+        self.bone_radius = 0.005
+
+    def query_mesh_rig(self):
+        
+        input_dict = {"shape": self.mesh}
+
+        # Create joints as spheres
+        joint_meshes = []
+        for i, joint in enumerate(self.joints):
+            
+            sphere = trimesh.creation.icosphere(
+                radius=self.joint_radius, subdivisions=2
+            )
+            sphere.apply_translation(joint)
+            if i == self.root_idx:
+                # root green
+                sphere.visual.vertex_colors = [0, 255, 0, 255]
+            else:
+                sphere.visual.vertex_colors = [0, 0, 255, 255]
+            
+            joint_meshes.append(sphere)
+        input_dict["joint_meshes"] = trimesh.util.concatenate(joint_meshes)
+
+        # Create bones as cylinders
+        bone_meshes = []
+        for bone in self.bones:
+            start, end = self.joints[bone[0]], self.joints[bone[1]]
+            cylinder = trimesh.creation.cylinder(radius=self.bone_radius, segment=np.array([[0, 0, 0], end - start]))
+            cylinder.apply_translation(start)
+            cylinder.visual.vertex_colors = [255, 0, 0, 255]  #[0, 0, 255, 255]  # blue
+            bone_meshes.append(cylinder)
+        input_dict["bone_meshes"] = trimesh.util.concatenate(bone_meshes)
+
+        return input_dict

data_utils/issue_data_list.txt

@@ -0,0 +1,123 @@
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+ecbc08ea-5f9d-5d2f-a496-77ec128bd3fe

data_utils/pyrender_wrapper.py

@@ -0,0 +1,135 @@
+# Modified from https://github.com/lab4d-org/lab4d
+
+import os
+import numpy as np
+import cv2
+import pyrender
+import trimesh
+from pyrender import (
+    IntrinsicsCamera,
+    Mesh,
+    Node,
+    Scene,
+    OffscreenRenderer,
+    MetallicRoughnessMaterial,
+    RenderFlags
+)
+
+os.environ["PYOPENGL_PLATFORM"] = "egl"
+
+def look_at(eye, center, up):
+    """Create a look-at (view) matrix."""
+    f = np.array(center, dtype=np.float32) - np.array(eye, dtype=np.float32)
+    f /= np.linalg.norm(f)
+
+    u = np.array(up, dtype=np.float32)
+    u /= np.linalg.norm(u)
+
+    s = np.cross(f, u)
+    u = np.cross(s, f)
+
+    m = np.identity(4, dtype=np.float32)
+    m[0, :3] = s
+    m[1, :3] = u
+    m[2, :3] = -f
+    m[:3, 3] = -np.matmul(m[:3, :3], np.array(eye, dtype=np.float32))
+
+    return m
+
+class PyRenderWrapper:
+    def __init__(self, image_size=(1024, 1024)) -> None:
+        # renderer
+        self.image_size = image_size
+        render_size = max(image_size)
+        self.r = OffscreenRenderer(render_size, render_size)
+        self.intrinsics = IntrinsicsCamera(
+            render_size, render_size, render_size / 2, render_size / 2
+        )
+        # light
+        self.light_pose = np.eye(4)
+        self.set_light_topdown()
+        self.direc_l = pyrender.DirectionalLight(color=np.ones(3), intensity=5.0)
+        self.material = MetallicRoughnessMaterial(
+            roughnessFactor=0.75, metallicFactor=0.75, alphaMode="BLEND"
+        )
+        self.init_camera()
+
+    def init_camera(self):
+        self.flip_pose = np.eye(4)
+        self.set_camera(np.eye(4))
+
+    def set_camera(self, scene_to_cam):
+        # object to camera transforms
+        self.scene_to_cam = self.flip_pose @ scene_to_cam
+        
+    def set_light_topdown(self, gl=False):
+        # top down light, slightly closer to the camera
+        if gl:
+            rot = cv2.Rodrigues(np.asarray([-np.pi / 2, 0, 0]))[0]
+        else:
+            rot = cv2.Rodrigues(np.asarray([np.pi / 2, 0, 0]))[0]
+        self.light_pose[:3, :3] = rot
+
+    def align_light_to_camera(self):
+        self.light_pose = np.linalg.inv(self.scene_to_cam)
+
+    def set_intrinsics(self, intrinsics):
+        """
+        Args:
+            intrinsics: (4,) fx,fy,px,py
+        """
+        self.intrinsics = IntrinsicsCamera(
+            intrinsics[0], intrinsics[1], intrinsics[2], intrinsics[3]
+        )
+
+    def get_cam_to_scene(self):
+        cam_to_scene = np.eye(4)
+        cam_to_scene[:3, :3] = self.scene_to_cam[:3, :3].T
+        cam_to_scene[:3, 3] = -self.scene_to_cam[:3, :3].T @ self.scene_to_cam[:3, 3]
+        return cam_to_scene
+    
+    def set_camera_view(self, angle, bbox_center, distance=2.0):
+        # Calculate camera position based on angle and distance from bounding box center
+        camera_position = bbox_center + distance * np.array([np.sin(angle), 0, np.cos(angle)], dtype=np.float32)
+        look_at_matrix = look_at(camera_position, bbox_center, [0, 1, 0])
+        self.scene_to_cam = look_at_matrix @ self.flip_pose
+    
+    def render(self, input_dict):
+        # Create separate scenes for transparent objects (mesh) and solid objects (joints and bones)
+        scene_transparent = Scene(ambient_light=np.array([1.0, 1.0, 1.0, 1.0]) * 0.1)
+        scene_solid = Scene(ambient_light=np.array([1.0, 1.0, 1.0, 1.0]) * 0.1)
+
+        mesh_pyrender = Mesh.from_trimesh(input_dict["shape"], smooth=False)
+        mesh_pyrender.primitives[0].material = self.material
+        scene_transparent.add(mesh_pyrender, pose=np.eye(4), name="shape")
+
+        if "joint_meshes" in input_dict:
+            joints_pyrender = Mesh.from_trimesh(input_dict["joint_meshes"], smooth=False)
+            joints_pyrender.primitives[0].material = self.material
+            scene_solid.add(joints_pyrender, pose=np.eye(4), name="joints")
+
+        if "bone_meshes" in input_dict:
+            bones_pyrender = Mesh.from_trimesh(input_dict["bone_meshes"], smooth=False)
+            bones_pyrender.primitives[0].material = self.material
+            scene_solid.add(bones_pyrender, pose=np.eye(4), name="bones")
+
+        # Camera for both scenes
+        scene_transparent.add(self.intrinsics, pose=self.get_cam_to_scene())
+        scene_solid.add(self.intrinsics, pose=self.get_cam_to_scene())
+
+        # Light for both scenes
+        scene_transparent.add(self.direc_l, pose=self.light_pose)
+        scene_solid.add(self.direc_l, pose=self.light_pose)
+
+        # Render transparent scene first
+        color_transparent, depth_transparent = self.r.render(scene_transparent)
+
+        # Render solid scene on top
+        color_solid, depth_solid = self.r.render(scene_solid)
+
+        # Combine the two scenes
+        color_combined = np.where(depth_solid[..., np.newaxis] == 0, color_transparent, color_solid)
+
+        return color_combined, depth_solid
+    def delete(self):
+        self.r.delete()

data_utils/read_npz.py

@@ -0,0 +1,43 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import numpy as np
+import scipy.sparse as sp
+
+# Load the NPZ file
+data = np.load('articulation_xlv2_test.npz', allow_pickle=True)
+data_list = data['arr_0']
+
+print(f"Loaded {len(data_list)} data entries")
+print(f"Data keys: {data_list[0].keys()}")
+# 'vertices', 'faces', 'normals', 'joints', 'bones', 'root_index', 'uuid', 'pc_w_norm', 'joint_names', 'skinning_weights_value', 
+# 'skinning_weights_row', 'skinning_weights_col', 'skinning_weights_shape'
+
+data = data_list[0] # check the first data
+
+vertices = data['vertices'] # (n_vertex, 3)
+faces = data['faces'] # (n_faces, 3)
+normals = data['normals'] # (n_vertex, 3)
+joints = data['joints'] # (n_joints, 3)
+bones = data['bones'] # (n_bones, 2)
+pc_w_norm = data['pc_w_norm'] # (8192, 6)
+
+# Extract the sparse skinning weights components
+skinning_data = data['skinning_weights_value']
+skinning_rows = data['skinning_weights_row']
+skinning_cols = data['skinning_weights_col']
+skinning_shape = data['skinning_weights_shape']
+
+skinning_sparse = sp.coo_matrix((skinning_data, (skinning_rows, skinning_cols)), shape=skinning_shape)
+skinning_weights = skinning_sparse.toarray()  # (n_vertex, n_joints)
+

data_utils/read_rig_mesh_from_glb.py

@@ -0,0 +1,198 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+"""
+Blender script for extracting rig (.txt) and mesh (.obj) from glbs. 
+This code currently supports GLB files only, but it can be easily modified to load other formats (e.g., FBX, DAE) with minimal changes.
+"""
+
+import bpy
+import os
+import re
+import json
+import pickle
+
+def get_hierarchy_root_joint(joint):
+    """
+    Function to find the top parent joint node from the given 
+    'joint' Blender node (armature bone).
+    """
+    root_joint = joint
+    while root_joint.parent is not None:
+        root_joint = root_joint.parent
+    return root_joint
+
+def get_meshes_and_armatures():
+    """
+    Function to get all meshes and armatures in the scene
+    """
+    default_objects = ['Cube', 'Light', 'Camera', 'Icosphere']
+    for obj_name in default_objects:
+        if obj_name in bpy.data.objects:
+            bpy.data.objects.remove(bpy.data.objects[obj_name], do_unlink=True)
+    
+    meshes = [obj for obj in bpy.context.scene.objects if obj.type == 'MESH']
+    armatures = [obj for obj in bpy.context.scene.objects if obj.type == 'ARMATURE']
+    return meshes, armatures
+
+def get_joint_dict(root):
+    """
+    Function to create a dictionary of joints from the root joint
+    """
+    joint_pos = {}
+    def traverse_bone(bone):
+        joint_pos[bone.name] = {
+            'pos': bone.head_local,
+            'pa': bone.parent.name if bone.parent else 'None',
+            'ch': [child.name for child in bone.children]
+        }
+        for child in bone.children:
+            traverse_bone(child)
+
+    traverse_bone(root)
+    return joint_pos
+
+def record_info(root, joint_dict, meshes, mesh_vert_offsets, file_info):
+    """
+    - root: root joint
+    - joint_dict
+    - meshes
+    - mesh_vert_offsets: for multi-geometry
+    - file_info
+    """
+    skin_records = {}
+
+    def replace_special_characters(name):
+        return re.sub(r'\W+', '_', name)
+
+    for key, val in joint_dict.items():
+        modified_key = replace_special_characters(key)
+        file_info.write(f'joints {modified_key} {val["pos"][0]:.8f} {val["pos"][1]:.8f} {val["pos"][2]:.8f}\n')
+    file_info.write(f'root {replace_special_characters(root.name)}\n')
+    
+    for mesh_index, mesh in enumerate(meshes):
+        vert_offset = mesh_vert_offsets[mesh_index]
+        if mesh.type == 'MESH':
+            for vtx in mesh.data.vertices:
+                weights = {}
+                for group in vtx.groups:
+                    bone_name = replace_special_characters(mesh.vertex_groups[group.group].name)
+                    weights[bone_name] = group.weight
+
+                global_vertex_index = vert_offset + vtx.index
+
+                skin_record = f"skin {global_vertex_index} " + " ".join(f"{bone} {weight:.4f}" for bone, weight in weights.items())
+                
+                if global_vertex_index not in skin_records:
+                    skin_records[global_vertex_index] = skin_record
+                    file_info.write(skin_record + "\n")
+    
+    for key, val in joint_dict.items():
+        if val['pa'] != 'None':
+            parent_name = replace_special_characters(val['pa'])
+            child_name = replace_special_characters(key)
+            file_info.write(f'hier {parent_name} {child_name}\n')
+
+
+def record_obj(meshes, file_obj):
+    vert_offset = 0
+    norm_offset = 0
+    mesh_vert_offsets = []
+
+    for mesh in meshes:
+        mesh_vert_offsets.append(vert_offset)
+        bpy.context.view_layer.objects.active = mesh
+        bpy.ops.object.mode_set(mode='OBJECT')
+        
+        # vertex
+        for v in mesh.data.vertices:
+            file_obj.write(f"v {v.co[0]} {v.co[1]} {v.co[2]}\n")
+        file_obj.write("\n")
+        
+        # normal
+        for vn in mesh.data.vertices:
+            normal = vn.normal
+            file_obj.write(f"vn {normal[0]} {normal[1]} {normal[2]}\n")
+        file_obj.write("\n")
+        
+        # face
+        for poly in mesh.data.polygons:
+            verts = [v + 1 + vert_offset for v in poly.vertices] 
+            file_obj.write(f"f {verts[0]}//{verts[0]} {verts[1]}//{verts[1]} {verts[2]}//{verts[2]}\n")
+        
+        vert_count = len(mesh.data.vertices)
+        vert_offset += vert_count
+        norm_offset += vert_count
+
+    return mesh_vert_offsets
+
+def process_glb(glb_path, rigs_dir, meshes_dir):
+    base_name = os.path.splitext(os.path.basename(glb_path))[0]
+    
+    obj_name = os.path.join(meshes_dir, f'{base_name}.obj')
+    info_name = os.path.join(rigs_dir, f'{base_name}.txt')
+    
+    # Skip processing if rig info file already exists
+    if os.path.exists(info_name):
+        print(f"{info_name} already exists. Skipping...")
+        return
+    
+    if os.path.exists(obj_name):
+        print(f"{obj_name} already exists. Skipping...")
+        return
+    
+    bpy.ops.wm.read_factory_settings(use_empty=True)
+    bpy.ops.import_scene.gltf(filepath=glb_path)
+    
+    meshes, armatures = get_meshes_and_armatures()
+    
+    if not armatures:
+        print(f"No armatures found in {glb_path}. Skipping...")
+        return
+    
+    root = armatures[0].data.bones[0]
+    root_name = get_hierarchy_root_joint(root)
+    joint_dict = get_joint_dict(root_name)
+    
+    #  save meshes
+    with open(obj_name, 'w') as file_obj:
+        mesh_vert_offsets = record_obj(meshes, file_obj)
+    
+    # save rigs
+    with open(info_name, 'w') as file_info:
+        record_info(root_name, joint_dict, meshes, mesh_vert_offsets, file_info)
+    
+    print(f"Processed {glb_path}")
+
+if __name__ == '__main__':
+    
+    src_dir = 'glbs'
+    rigs_dir = 'rigs'
+    meshes_dir = 'meshes'
+    # Ensure rigs directory exists
+    if not os.path.exists(rigs_dir):
+        os.makedirs(rigs_dir)
+    if not os.path.exists(meshes_dir):
+        os.makedirs(meshes_dir)
+
+    glb_paths = [os.path.join(src_dir, file) for file in os.listdir(src_dir) if file.endswith('.glb')]
+    
+    print(len(glb_paths))
+    
+    for glb_path in glb_paths:
+        try:
+            process_glb(glb_path, rigs_dir, meshes_dir)
+        except Exception as e:
+            with open('error.txt', 'a') as error_file:
+                error_file.write(f"{glb_path}: {str(e)}\n")

data_utils/render_data.py

@@ -0,0 +1,61 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import os
+import numpy as np
+import cv2
+
+from pyrender_wrapper import PyRenderWrapper
+from data_loader import DataLoader
+
+def main():
+    loader = DataLoader()
+    
+    raw_size = (960, 960)
+    renderer = PyRenderWrapper(raw_size)
+    
+    output_dir = 'render_results'
+    os.makedirs(output_dir, exist_ok=True)
+    
+    rig_path = 'examples/0a59c5ffa4a1476bac6d540b79947f31.txt'
+    mesh_path = rig_path.replace('.txt', '.obj')
+    
+    filename = os.path.splitext(os.path.basename(rig_path))[0]
+
+    loader.load_rig_data(rig_path)
+    loader.load_mesh(mesh_path)
+    input_dict = loader.query_mesh_rig()
+
+    angles = [0, np.pi/2, np.pi, 3*np.pi/2] 
+    
+    bbox_center = loader.mesh.bounding_box.centroid
+    bbox_size = loader.mesh.bounding_box.extents
+    distance = np.max(bbox_size) * 2
+    
+    subfolder_path = os.path.join(output_dir, filename)
+    
+    os.makedirs(subfolder_path, exist_ok=True)
+    
+    for i, angle in enumerate(angles):
+        print(f"Rendering view at {np.degrees(angle)} degrees")
+        
+        renderer.set_camera_view(angle, bbox_center, distance)
+        renderer.align_light_to_camera()
+
+        color = renderer.render(input_dict)[0]
+        
+        output_filename = f"{filename}_view{i+1}.png"
+        output_filepath = os.path.join(subfolder_path, output_filename)
+        cv2.imwrite(output_filepath, color)
+if __name__ == "__main__":
+    main()

data_utils/save_npz.py

@@ -0,0 +1,252 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""
+This python script shows how we process the meshes and rigs from the input folders and save them in a compressed npz file.
+"""
+import os
+import numpy as np
+import glob
+import pickle
+from concurrent.futures import ProcessPoolExecutor
+import skimage.measure
+import trimesh
+import mesh2sdf.core
+import scipy.sparse as sp
+
+def read_obj_file(file_path):
+    vertices = []
+    faces = []
+    normals = []  # Added normals list
+    
+    with open(file_path, 'r') as file:
+        for line in file:
+            if line.startswith('v '):
+                parts = line.split()[1:]
+                vertices.append([float(parts[0]), float(parts[1]), float(parts[2])])
+            elif line.startswith('vn '):  # Added reading normals
+                parts = line.split()[1:]
+                normals.append([float(parts[0]), float(parts[1]), float(parts[2])])
+            elif line.startswith('f '):
+                parts = line.split()[1:]
+                # OBJ format is 1-based, we need 0-based for npz
+                face = [int(part.split('//')[0]) - 1 for part in parts]
+                faces.append(face)
+    
+    return np.array(vertices), np.array(faces), np.array(normals) 
+
+def read_rig_file(file_path):
+    """
+    Read rig from txt file, our format is the same as RigNet: 
+    joints joint_name x y z
+    root root_joint_name
+    skin vertex_idx joint_name weight joint_name weight ...
+    hier parent_joint_name child_joint_name
+    """
+    joints = []
+    bones = []
+    joint_names = [] 
+
+    joint_mapping = {}
+    joint_index = 0
+    
+    skinning_data = {}  # Dictionary to store vertex index -> [(joint_idx, weight), ...]
+
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+
+    for line in lines:
+        parts = line.split()
+        if line.startswith('joints'):
+            name = parts[1]
+            position = [float(parts[2]), float(parts[3]), float(parts[4])]
+            joints.append(position)
+            joint_names.append(name) 
+            joint_mapping[name] = joint_index
+            joint_index += 1
+        elif line.startswith('hier'):
+            parent_joint = joint_mapping[parts[1]]
+            child_joint = joint_mapping[parts[2]]
+            bones.append([parent_joint, child_joint])
+        elif line.startswith('root'):
+            root = joint_mapping[parts[1]]
+        elif line.startswith('skin'):
+            vertex_idx = int(parts[1])
+            
+            if vertex_idx not in skinning_data:
+                skinning_data[vertex_idx] = []
+
+            for i in range(2, len(parts), 2):
+                if i+1 < len(parts):  
+                    joint_name = parts[i]
+                    weight = float(parts[i+1])
+                    
+                    if joint_name in joint_mapping:
+                        joint_idx = joint_mapping[joint_name]
+                        skinning_data[vertex_idx].append((joint_idx, weight))
+
+    return np.array(joints), np.array(bones), root, joint_names, skinning_data
+
+def convert_to_sparse_skinning(skinning_data, num_vertices, num_joints):
+    """Convert skinning weights to sparse matrix format."""
+    rows = []
+    cols = []
+    data = []
+    
+    for vertex_idx, weights in skinning_data.items():
+        for joint_idx, weight in weights:
+            rows.append(vertex_idx)
+            cols.append(joint_idx)
+            data.append(weight)
+    
+    sparse_skinning = sp.coo_matrix((data, (rows, cols)), shape=(num_vertices, num_joints))
+    
+    # Return as tuple of arrays which can be serialized
+    return (sparse_skinning.data, sparse_skinning.row, sparse_skinning.col, sparse_skinning.shape)
+
+def normalize_to_unit_cube(vertices, normals=None, scale_factor=1.0):
+    min_coords = vertices.min(axis=0)
+    max_coords = vertices.max(axis=0)
+    center = (max_coords + min_coords) / 2.0
+    
+    vertices -= center
+    scale = 1.0 / np.abs(vertices).max() * scale_factor
+    vertices *= scale
+    
+    if normals is not None:
+        # Normalize each normal vector to unit length
+        norms = np.linalg.norm(normals, axis=1, keepdims=True)
+        normals = normals / (norms+1e-8)
+    
+        return vertices, normals, center, scale
+    else:
+        return vertices, center, scale
+
+def normalize_vertices(vertices, scale=0.9):
+    bbmin, bbmax = vertices.min(0), vertices.max(0)
+    center = (bbmin + bbmax) * 0.5
+    scale = 2.0 * scale / (bbmax - bbmin).max()
+    vertices = (vertices - center) * scale
+    return vertices, center, scale
+
+def export_to_watertight(normalized_mesh, octree_depth: int = 7):
+    """
+        Convert the non-watertight mesh to watertight.
+
+        Args:
+            input_path (str): normalized path
+            octree_depth (int):
+
+        Returns:
+            mesh(trimesh.Trimesh): watertight mesh
+
+        """
+    size = 2 ** octree_depth
+    level = 2 / size
+
+    scaled_vertices, to_orig_center, to_orig_scale = normalize_vertices(normalized_mesh.vertices)
+
+    sdf = mesh2sdf.core.compute(scaled_vertices, normalized_mesh.faces, size=size)
+
+    vertices, faces, normals, _ = skimage.measure.marching_cubes(np.abs(sdf), level)
+
+    # watertight mesh
+    vertices = vertices / size * 2 - 1 # -1 to 1
+    vertices = vertices / to_orig_scale + to_orig_center
+    mesh = trimesh.Trimesh(vertices, faces, normals=normals)
+
+    return mesh
+
+def process_mesh_to_pc(mesh, marching_cubes = True, sample_num = 8192):
+    if marching_cubes:
+        mesh = export_to_watertight(mesh)
+    return_mesh = mesh
+    points, face_idx = mesh.sample(sample_num, return_index=True)
+    points, _, _ = normalize_to_unit_cube(points, scale_factor=0.9995)
+    normals = mesh.face_normals[face_idx]
+
+    pc_normal = np.concatenate([points, normals], axis=-1, dtype=np.float16)
+    return pc_normal, return_mesh
+
+def process_single_file(args):
+    mesh_file, rig_file = args
+    mesh_name = os.path.basename(mesh_file).split('.')[0]
+    rig_name = os.path.basename(rig_file).split('.')[0]
+
+    if mesh_name != rig_name:
+        print(f"Skipping files {mesh_file} and {rig_file} because their names do not match.")
+        return None
+    
+    vertices, faces, normals = read_obj_file(mesh_file)
+
+    joints, bones, root, joint_names, skinning_data = read_rig_file(rig_file)
+
+    # Normalize the mesh to the unit cube centered at the origin
+    vertices, normals, center, scale = normalize_to_unit_cube(vertices, normals, scale_factor=0.5)
+    
+    # Apply the same transformation to joints
+    joints -= center
+    joints *= scale
+
+    # Create trimesh object for processing
+    mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
+
+    # Process into point cloud with normals
+    pc_normal, _ = process_mesh_to_pc(mesh)
+    
+    # Convert skinning data to sparse format
+    sparse_skinning = convert_to_sparse_skinning(skinning_data, len(vertices), len(joints))
+
+    return {
+        'vertices': vertices,
+        'faces': faces,
+        'normals': normals,
+        'joints': joints,
+        'bones': bones,
+        'root_index': root,
+        'uuid': mesh_name,
+        'pc_w_norm': pc_normal,
+        'joint_names': joint_names,
+        'skinning_weights_value': sparse_skinning[0],  # values
+        'skinning_weights_rows': sparse_skinning[1],  # row indices
+        'skinning_weights_cols': sparse_skinning[2],  # column indices
+        'skinning_weights_shape': sparse_skinning[3]  # shape of matrix
+    }
+        
+def process_files(mesh_folder, rig_folder, output_file, num_workers=8):
+    file_pairs = []
+
+    for root, _, files in os.walk(rig_folder):
+        for file in files:
+            if file.endswith('.txt'):
+                rig_file = os.path.join(root, file)
+                obj_base_name = os.path.splitext(file)[0]
+                mesh_file = os.path.join(mesh_folder, obj_base_name + '.obj')
+                if os.path.exists(mesh_file):
+                    file_pairs.append((mesh_file, rig_file))
+                else:
+                    print(f"Mesh file not found: {mesh_file}")
+
+    with ProcessPoolExecutor(max_workers=num_workers) as executor:
+        data_list = list(executor.map(process_single_file, file_pairs))
+    
+    data_list = [data for data in data_list if data is not None]
+    
+    np.savez_compressed(output_file, data_list, allow_pickle=True) 
+
+# Example usage
+mesh_folder = 'meshes/'
+rig_folder = 'rigs/'
+output_file = 'results.npz'
+
+process_files(mesh_folder, rig_folder, output_file)

data_utils/update_npz_rm_issue_data.py

@@ -0,0 +1,59 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import numpy as np
+import os
+
+def filter_npz_by_filenames(npz_path, txt_path, output_path):
+    
+    data_list = np.load(npz_path, allow_pickle=True)['arr_0']
+   
+    with open(txt_path, 'r') as f:
+        exclude_filenames = set(line.strip() for line in f if line.strip())
+
+    # Filter the data list
+    filtered_data = []
+    excluded_count = 0
+    
+    for item in data_list:
+            
+        filename = item['uuid']
+        
+        if filename in exclude_filenames:
+            excluded_count += 1
+            print(filename)
+        else:
+            filtered_data.append(item)
+    
+    # Save the filtered data
+    kept_count = len(filtered_data)
+    total_count = len(data_list)
+    print(f"Original items: {total_count}")
+    print(f"Kept items: {kept_count}")
+    print(f"Removed items: {excluded_count}")
+    
+    print(f"Saving filtered data")
+    np.savez_compressed(output_path, filtered_data, allow_pickle=True) 
+
+def main():
+    issue_list = "data_utils/issue_data_list.txt"  # Change this to your text file path
+    npz_path_train = "articulation_xlv2_train.npz"  # Change this to your NPZ file path
+    output_path_train = "articulation_xlv2_train_update.npz"
+    npz_path_test = "articulation_xlv2_test.npz"  # Change this to your NPZ file path
+    output_path_test = "articulation_xlv2_test_update.npz"
+    
+    filter_npz_by_filenames(npz_path_train, issue_list, output_path_train)
+    filter_npz_by_filenames(npz_path_test, issue_list, output_path_test)
+        
+if __name__ == "__main__":
+    main()

download_models.py

@@ -0,0 +1,80 @@
+"""
+自动下载MagicArticulate和Michelangelo所需的模型文件
+在HF Space启动时调用
+"""
+
+import os
+import logging
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+def download_models():
+    """下载所有必需的模型文件"""
+    try:
+        from huggingface_hub import hf_hub_download
+        
+        logger.info("🔄 开始下载模型文件...")
+        
+        # 1. 下载Michelangelo模型
+        michelangelo_path = "third_party/Michelangelo/checkpoints/aligned_shape_latents/shapevae-256.ckpt"
+        if not os.path.exists(michelangelo_path):
+            logger.info("📥 下载Michelangelo模型...")
+            try:
+                file_path = hf_hub_download(
+                    repo_id="Maikou/Michelangelo",
+                    filename="checkpoints/aligned_shape_latents/shapevae-256.ckpt",
+                    local_dir="third_party/Michelangelo"
+                )
+                logger.info(f"✅ Michelangelo模型下载完成: {file_path}")
+            except Exception as e:
+                logger.error(f"❌ Michelangelo模型下载失败: {e}")
+        else:
+            logger.info("✅ Michelangelo模型已存在")
+        
+        # 2. 下载MagicArticulate层次模型
+        hier_path = "skeleton_ckpt/checkpoint_trainonv2_hier.pth"
+        if not os.path.exists(hier_path):
+            logger.info("📥 下载MagicArticulate层次模型...")
+            try:
+                os.makedirs("skeleton_ckpt", exist_ok=True)
+                file_path = hf_hub_download(
+                    repo_id="Seed3D/MagicArticulate",
+                    filename="skeleton_ckpt/checkpoint_trainonv2_hier.pth",
+                    local_dir=""
+                )
+                logger.info(f"✅ MagicArticulate层次模型下载完成: {file_path}")
+            except Exception as e:
+                logger.error(f"❌ MagicArticulate层次模型下载失败: {e}")
+        else:
+            logger.info("✅ MagicArticulate层次模型已存在")
+        
+        # 3. 下载MagicArticulate空间模型
+        spatial_path = "skeleton_ckpt/checkpoint_trainonv2_spatial.pth"
+        if not os.path.exists(spatial_path):
+            logger.info("📥 下载MagicArticulate空间模型...")
+            try:
+                os.makedirs("skeleton_ckpt", exist_ok=True)
+                file_path = hf_hub_download(
+                    repo_id="Seed3D/MagicArticulate",
+                    filename="skeleton_ckpt/checkpoint_trainonv2_spatial.pth",
+                    local_dir=""
+                )
+                logger.info(f"✅ MagicArticulate空间模型下载完成: {file_path}")
+            except Exception as e:
+                logger.error(f"❌ MagicArticulate空间模型下载失败: {e}")
+        else:
+            logger.info("✅ MagicArticulate空间模型已存在")
+        
+        logger.info("🎯 模型下载过程完成")
+        return True
+        
+    except ImportError:
+        logger.error("❌ huggingface_hub未安装，无法下载模型")
+        return False
+    except Exception as e:
+        logger.error(f"💥 模型下载过程出错: {e}")
+        return False
+
+if __name__ == "__main__":
+    download_models()

magic_articulate_plus/__init__.py

@@ -0,0 +1,20 @@
+"""
+MagicArticulate-Plus Integration
+集成用户上传支持和增强功能
+"""
+
+from .articulate_api import (
+    MagicArticulateAPI,
+    ModelValidator,
+    ModelPreprocessor,
+    UserSessionManager,
+    process_model_file
+)
+
+__all__ = [
+    'MagicArticulateAPI',
+    'ModelValidator', 
+    'ModelPreprocessor',
+    'UserSessionManager',
+    'process_model_file'
+]

magic_articulate_plus/articulate_api.py

@@ -0,0 +1,899 @@
+#!/usr/bin/env python3
+"""
+MagicArticulate API - Enhanced Version
+支持用户上传的3D模型文件和多用户结果管理
+"""
+
+import os
+import sys
+import uuid
+import json
+import time
+import shutil
+import logging
+import tempfile
+import traceback
+from pathlib import Path
+from datetime import datetime
+from typing import Dict, Any, List, Optional, Tuple
+
+import torch
+import trimesh
+import numpy as np
+from tqdm import tqdm
+
+from accelerate import Accelerator
+from accelerate.utils import set_seed, DistributedDataParallelKwargs
+
+# 添加父目录到路径以正确导入模块
+parent_dir = str(Path(__file__).parent.parent)
+if parent_dir not in sys.path:
+    sys.path.insert(0, parent_dir)
+
+print(f"🔍 ARTICULATE_API DEBUG: Current working directory: {os.getcwd()}")
+print(f"🔍 ARTICULATE_API DEBUG: Script file path: {__file__}")
+print(f"🔍 ARTICULATE_API DEBUG: Parent directory: {parent_dir}")
+print(f"🔍 ARTICULATE_API DEBUG: sys.path includes:")
+for i, path in enumerate(sys.path[:10]):  # 只显示前10个避免太长
+    print(f"  {i}: {path}")
+
+# 检查目录结构
+utils_path = os.path.join(parent_dir, 'utils')
+skeleton_path = os.path.join(parent_dir, 'skeleton_models')
+print(f"🔍 ARTICULATE_API DEBUG: utils path exists: {os.path.exists(utils_path)}")
+print(f"🔍 ARTICULATE_API DEBUG: skeleton_models path exists: {os.path.exists(skeleton_path)}")
+
+if os.path.exists(utils_path):
+    print(f"🔍 ARTICULATE_API DEBUG: utils contents: {os.listdir(utils_path)}")
+
+from skeleton_models.skeletongen import SkeletonGPT
+from utils.mesh_to_pc import MeshProcessor
+from utils.save_utils import (
+    save_mesh, pred_joints_and_bones, save_skeleton_to_txt, 
+    save_args, remove_duplicate_joints, save_skeleton_obj, 
+    render_mesh_with_skeleton
+)
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class ModelValidator:
+    """3D模型验证和修复类"""
+    
+    SUPPORTED_FORMATS = {'.obj', '.glb', '.gltf', '.ply', '.stl', '.fbx', '.dae'}
+    MAX_VERTICES = 100000  # 最大顶点数
+    MIN_VERTICES = 100     # 最小顶点数
+    MAX_FILE_SIZE_MB = 100 # 最大文件大小
+    
+    @staticmethod
+    def validate_file(file_path: str) -> Tuple[bool, str, Dict[str, Any]]:
+        """
+        验证3D模型文件
+        
+        Returns:
+            (is_valid, error_message, model_info)
+        """
+        try:
+            # 检查文件是否存在
+            if not os.path.exists(file_path):
+                return False, "文件不存在", {}
+            
+            # 检查文件大小
+            file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
+            if file_size_mb > ModelValidator.MAX_FILE_SIZE_MB:
+                return False, f"文件过大: {file_size_mb:.1f}MB > {ModelValidator.MAX_FILE_SIZE_MB}MB", {}
+            
+            # 检查文件格式
+            file_ext = Path(file_path).suffix.lower()
+            if file_ext not in ModelValidator.SUPPORTED_FORMATS:
+                return False, f"不支持的文件格式: {file_ext}", {}
+            
+            # 尝试加载模型
+            mesh = trimesh.load(file_path, force='mesh')
+            
+            # 检查是否为有效网格
+            if not hasattr(mesh, 'vertices') or not hasattr(mesh, 'faces'):
+                return False, "文件不包含有效的网格数据", {}
+            
+            # 检查顶点数量
+            vertex_count = len(mesh.vertices)
+            if vertex_count < ModelValidator.MIN_VERTICES:
+                return False, f"顶点数量过少: {vertex_count} < {ModelValidator.MIN_VERTICES}", {}
+            if vertex_count > ModelValidator.MAX_VERTICES:
+                return False, f"顶点数量过多: {vertex_count} > {ModelValidator.MAX_VERTICES}", {}
+            
+            # 收集模型信息
+            model_info = {
+                'file_name': os.path.basename(file_path),
+                'file_size_mb': file_size_mb,
+                'format': file_ext,
+                'vertex_count': vertex_count,
+                'face_count': len(mesh.faces) if hasattr(mesh, 'faces') else 0,
+                'bounds': mesh.bounds.tolist() if hasattr(mesh, 'bounds') else None,
+                'is_watertight': mesh.is_watertight if hasattr(mesh, 'is_watertight') else False,
+                'volume': float(mesh.volume) if hasattr(mesh, 'volume') else 0.0,
+                'area': float(mesh.area) if hasattr(mesh, 'area') else 0.0,
+            }
+            
+            return True, "验证通过", model_info
+            
+        except Exception as e:
+            return False, f"模型验证失败: {str(e)}", {}
+    
+    @staticmethod
+    def auto_repair_mesh(mesh: trimesh.Trimesh) -> Tuple[trimesh.Trimesh, List[str]]:
+        """
+        自动修复网格问题
+        
+        Returns:
+            (repaired_mesh, repair_log)
+        """
+        repair_log = []
+        
+        try:
+            # 移除重复顶点
+            if mesh.is_volume:
+                original_vertices = len(mesh.vertices)
+                mesh.merge_vertices()
+                if len(mesh.vertices) < original_vertices:
+                    repair_log.append(f"移除了 {original_vertices - len(mesh.vertices)} 个重复顶点")
+            
+            # 修复法向量
+            if not hasattr(mesh, 'vertex_normals') or mesh.vertex_normals is None:
+                mesh.fix_normals()
+                repair_log.append("修复了顶点法向量")
+            
+            # 移除退化面
+            original_faces = len(mesh.faces)
+            mesh.remove_degenerate_faces()
+            if len(mesh.faces) < original_faces:
+                repair_log.append(f"移除了 {original_faces - len(mesh.faces)} 个退化面")
+            
+            # 填充孔洞（如果需要）
+            if not mesh.is_watertight and hasattr(mesh, 'fill_holes'):
+                try:
+                    mesh.fill_holes()
+                    repair_log.append("填充了网格孔洞")
+                except:
+                    repair_log.append("尝试填充孔洞失败，但继续处理")
+            
+            return mesh, repair_log
+            
+        except Exception as e:
+            logger.warning(f"网格修复过程中出现错误: {str(e)}")
+            return mesh, repair_log + [f"修复过程出错: {str(e)}"]
+
+class ModelPreprocessor:
+    """模型预处理类"""
+    
+    @staticmethod
+    def convert_format(input_path: str, output_format: str = '.obj') -> str:
+        """
+        转换模型格式
+        
+        Args:
+            input_path: 输入文件路径
+            output_format: 输出格式 (默认为.obj)
+            
+        Returns:
+            输出文件路径
+        """
+        try:
+            mesh = trimesh.load(input_path, force='mesh')
+            
+            # 生成输出路径
+            base_name = os.path.splitext(os.path.basename(input_path))[0]
+            output_path = os.path.join(
+                os.path.dirname(input_path), 
+                f"{base_name}_converted{output_format}"
+            )
+            
+            # 导出为指定格式
+            mesh.export(output_path)
+            
+            logger.info(f"格式转换完成: {input_path} -> {output_path}")
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"格式转换失败: {str(e)}")
+            raise
+    
+    @staticmethod
+    def simplify_mesh(mesh: trimesh.Trimesh, target_faces: int = 5000) -> trimesh.Trimesh:
+        """
+        简化网格
+        
+        Args:
+            mesh: 输入网格
+            target_faces: 目标面数
+            
+        Returns:
+            简化后的网格
+        """
+        try:
+            if len(mesh.faces) <= target_faces:
+                return mesh
+            
+            # 使用quadric decimation进行简化
+            simplified = mesh.simplify_quadratic_decimation(target_faces)
+            
+            logger.info(f"网格简化: {len(mesh.faces)} -> {len(simplified.faces)} 面")
+            return simplified
+            
+        except Exception as e:
+            logger.warning(f"网格简化失败: {str(e)}, 使用原始网格")
+            return mesh
+    
+    @staticmethod
+    def normalize_mesh(mesh: trimesh.Trimesh, scale_factor: float = 0.95) -> Tuple[trimesh.Trimesh, Dict[str, Any]]:
+        """
+        标准化网格到标准坐标空间
+        
+        Args:
+            mesh: 输入网格
+            scale_factor: 缩放因子
+            
+        Returns:
+            (normalized_mesh, transform_info)
+        """
+        try:
+            # 计算边界框
+            bounds = mesh.bounds
+            center = (bounds[0] + bounds[1]) / 2
+            size = bounds[1] - bounds[0]
+            max_size = size.max()
+            
+            # 计算变换参数
+            scale = (2.0 * scale_factor) / max_size
+            translation = -center
+            
+            # 应用变换
+            vertices = mesh.vertices.copy()
+            vertices = (vertices + translation) * scale
+            
+            # 创建新网格
+            normalized_mesh = trimesh.Trimesh(vertices=vertices, faces=mesh.faces)
+            
+            # 记录变换信息
+            transform_info = {
+                'original_center': center.tolist(),
+                'original_size': size.tolist(),
+                'scale': float(scale),
+                'translation': translation.tolist()
+            }
+            
+            logger.info(f"网格标准化完成: scale={scale:.4f}")
+            return normalized_mesh, transform_info
+            
+        except Exception as e:
+            logger.error(f"网格标准化失败: {str(e)}")
+            raise
+
+class UserSessionManager:
+    """用户会话管理类"""
+    
+    def __init__(self, base_dir: str = "user_sessions"):
+        self.base_dir = Path(base_dir)
+        self.base_dir.mkdir(exist_ok=True)
+        
+        # 元数据文件
+        self.metadata_file = self.base_dir / "sessions_metadata.json"
+        self.load_metadata()
+    
+    def load_metadata(self):
+        """加载会话元数据"""
+        if self.metadata_file.exists():
+            with open(self.metadata_file, 'r', encoding='utf-8') as f:
+                self.sessions = json.load(f)
+        else:
+            self.sessions = {}
+    
+    def save_metadata(self):
+        """保存会话元数据"""
+        with open(self.metadata_file, 'w', encoding='utf-8') as f:
+            json.dump(self.sessions, f, indent=2, ensure_ascii=False)
+    
+    def create_session(self, user_id: Optional[str] = None) -> str:
+        """
+        创建新的用户会话
+        
+        Args:
+            user_id: 用户ID（可选）
+            
+        Returns:
+            session_id
+        """
+        session_id = str(uuid.uuid4())
+        session_dir = self.base_dir / session_id
+        session_dir.mkdir(exist_ok=True)
+        
+        # 创建子目录
+        (session_dir / "uploads").mkdir(exist_ok=True)
+        (session_dir / "outputs").mkdir(exist_ok=True)
+        (session_dir / "temp").mkdir(exist_ok=True)
+        
+        # 记录会话信息
+        self.sessions[session_id] = {
+            'user_id': user_id,
+            'created_at': datetime.now().isoformat(),
+            'status': 'active',
+            'processed_models': [],
+            'last_activity': datetime.now().isoformat()
+        }
+        
+        self.save_metadata()
+        logger.info(f"创建新会话: {session_id}")
+        return session_id
+    
+    def get_session_dir(self, session_id: str) -> Path:
+        """获取会话目录"""
+        session_dir = self.base_dir / session_id
+        if not session_dir.exists():
+            raise ValueError(f"会话不存在: {session_id}")
+        return session_dir
+    
+    def update_activity(self, session_id: str):
+        """更新会话活动时间"""
+        if session_id in self.sessions:
+            self.sessions[session_id]['last_activity'] = datetime.now().isoformat()
+            self.save_metadata()
+    
+    def add_processed_model(self, session_id: str, model_info: Dict[str, Any]):
+        """添加已处理模型记录"""
+        if session_id in self.sessions:
+            self.sessions[session_id]['processed_models'].append(model_info)
+            self.update_activity(session_id)
+    
+    def cleanup_old_sessions(self, max_age_days: int = 7):
+        """清理旧会话"""
+        cutoff_time = datetime.now().timestamp() - (max_age_days * 24 * 3600)
+        
+        sessions_to_remove = []
+        for session_id, session_info in self.sessions.items():
+            last_activity = datetime.fromisoformat(session_info['last_activity'])
+            if last_activity.timestamp() < cutoff_time:
+                sessions_to_remove.append(session_id)
+        
+        for session_id in sessions_to_remove:
+            try:
+                session_dir = self.base_dir / session_id
+                if session_dir.exists():
+                    shutil.rmtree(session_dir)
+                del self.sessions[session_id]
+                logger.info(f"清理旧会话: {session_id}")
+            except Exception as e:
+                logger.error(f"清理会话失败 {session_id}: {str(e)}")
+        
+        if sessions_to_remove:
+            self.save_metadata()
+
+class MagicArticulateAPI:
+    """MagicArticulate API主类"""
+    
+    def __init__(self, 
+                 model_weights_path: Optional[str] = None,
+                 device: str = "auto",
+                 session_base_dir: str = "user_sessions"):
+        
+        self.device = self._setup_device(device)
+        self.model = None
+        self.accelerator = None
+        self.model_weights_path = model_weights_path
+        
+        # 初始化会话管理器
+        self.session_manager = UserSessionManager(session_base_dir)
+        
+        # 默认处理参数 - 匹配原始demo.py设置
+        self.default_args = {
+            'input_pc_num': 8192,
+            'num_beams': 1,
+            'n_discrete_size': 128,
+            'n_max_bones': 100,
+            'pad_id': -1,
+            'precision': 'fp16',
+            'batchsize_per_gpu': 1,
+            'apply_marching_cubes': False,
+            'octree_depth': 7,
+            'hier_order': False,  # 匹配demo.py默认值
+            'save_render': False,
+            'llm': 'facebook/opt-350m'  # 匹配demo.py默认值
+        }
+        
+        self.initialized = False
+        logger.info("MagicArticulate API 初始化完成")
+    
+    def _setup_device(self, device: str) -> torch.device:
+        """设置计算设备"""
+        if device == "auto":
+            if torch.cuda.is_available():
+                device = "cuda"
+                logger.info(f"使用GPU: {torch.cuda.get_device_name()}")
+            else:
+                device = "cpu"
+                logger.info("使用CPU")
+        
+        return torch.device(device)
+    
+    def initialize_model(self) -> bool:
+        """初始化模型"""
+        try:
+            if self.initialized:
+                return True
+            
+            logger.info("正在初始化MagicArticulate模型...")
+            
+            # 设置加速器
+            kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+            self.accelerator = Accelerator(
+                kwargs_handlers=[kwargs],
+                mixed_precision=self.default_args['precision'],
+            )
+            
+            # 创建模型
+            args = self._create_args_object()
+            self.model = SkeletonGPT(args)
+            
+            if self.device.type == "cuda":
+                self.model = self.model.cuda()
+            
+            # 加载预训练权重
+            if self.model_weights_path and os.path.exists(self.model_weights_path):
+                logger.info(f"加载模型权重: {self.model_weights_path}")
+                pkg = torch.load(self.model_weights_path, map_location=self.device)
+                self.model.load_state_dict(pkg["model"])
+            else:
+                error_msg = "预训练权重必须提供！当前使用随机初始化，结果将不准确。"
+                logger.error(error_msg)
+                # 不抛出错误，但给出强烈警告
+                logger.error("⚠️ WARNING: 没有预训练权重，生成的骨骼结构将不准确！")
+            
+            self.model.eval()
+            set_seed(0)
+            
+            # 准备模型
+            if self.accelerator:
+                self.model = self.accelerator.prepare(self.model)
+            
+            self.initialized = True
+            logger.info("✅ 模型初始化成功")
+            return True
+            
+        except Exception as e:
+            logger.error(f"❌ 模型初始化失败: {str(e)}")
+            logger.error(traceback.format_exc())
+            return False
+    
+    def process_uploaded_model(self,
+                             file_path: str,
+                             session_id: Optional[str] = None,
+                             user_prompt: str = "",
+                             processing_options: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """
+        处理用户上传的3D模型
+        
+        Args:
+            file_path: 模型文件路径
+            session_id: 会话ID（可选）
+            user_prompt: 用户提示词
+            processing_options: 处理选项
+            
+        Returns:
+            处理结果字典
+        """
+        start_time = time.time()
+        
+        try:
+            # 创建会话（如果未提供）
+            if not session_id:
+                session_id = self.session_manager.create_session()
+            
+            logger.info(f"开始处理模型: {file_path}, 会话: {session_id}")
+            
+            # 步骤1: 验证模型文件
+            is_valid, error_msg, model_info = ModelValidator.validate_file(file_path)
+            if not is_valid:
+                return self._create_error_result(error_msg, session_id, start_time)
+            
+            logger.info(f"模型验证通过: {model_info}")
+            
+            # 步骤2: 复制文件到会话目录
+            session_dir = self.session_manager.get_session_dir(session_id)
+            uploaded_file = session_dir / "uploads" / os.path.basename(file_path)
+            shutil.copy2(file_path, uploaded_file)
+            
+            # 步骤3: 预处理模型
+            processed_mesh, preprocessing_log = self._preprocess_model(
+                str(uploaded_file), 
+                processing_options or {}
+            )
+            
+            # 步骤4: 生成骨骼
+            if not self.initialized:
+                if not self.initialize_model():
+                    return self._create_error_result("模型初始化失败", session_id, start_time)
+            
+            skeleton_result = self._generate_skeleton(
+                processed_mesh, 
+                model_info['file_name'],
+                user_prompt
+            )
+            
+            # 步骤5: 保存结果
+            output_files = self._save_results(
+                skeleton_result,
+                processed_mesh,
+                model_info,
+                session_dir,
+                user_prompt
+            )
+            
+            # 步骤6: 记录处理结果
+            processing_time = time.time() - start_time
+            result = {
+                'success': True,
+                'session_id': session_id,
+                'processing_time': processing_time,
+                'model_info': model_info,
+                'preprocessing_log': preprocessing_log,
+                'skeleton_data': skeleton_result,
+                'output_files': output_files,
+                'user_prompt': user_prompt,
+                'timestamp': datetime.now().isoformat()
+            }
+            
+            # 更新会话记录
+            self.session_manager.add_processed_model(session_id, {
+                'file_name': model_info['file_name'],
+                'processing_time': processing_time,
+                'timestamp': datetime.now().isoformat(),
+                'success': True
+            })
+            
+            logger.info(f"✅ 模型处理完成，耗时: {processing_time:.2f}秒")
+            return result
+            
+        except Exception as e:
+            processing_time = time.time() - start_time
+            error_msg = f"处理过程中发生错误: {str(e)}"
+            logger.error(f"❌ {error_msg}")
+            logger.error(traceback.format_exc())
+            
+            return self._create_error_result(error_msg, session_id, start_time)
+    
+    def _preprocess_model(self, file_path: str, options: Dict[str, Any]) -> Tuple[trimesh.Trimesh, List[str]]:
+        """预处理模型"""
+        preprocessing_log = []
+        
+        try:
+            # 加载模型
+            mesh = trimesh.load(file_path, force='mesh')
+            preprocessing_log.append(f"加载模型: {len(mesh.vertices)} 顶点, {len(mesh.faces)} 面")
+            
+            # 自动修复
+            if options.get('auto_repair', True):
+                mesh, repair_log = ModelValidator.auto_repair_mesh(mesh)
+                preprocessing_log.extend(repair_log)
+            
+            # 简化网格（如果需要）
+            target_faces = options.get('target_faces', 10000)
+            if len(mesh.faces) > target_faces:
+                mesh = ModelPreprocessor.simplify_mesh(mesh, target_faces)
+                preprocessing_log.append(f"简化网格到 {len(mesh.faces)} 面")
+            
+            # 标准化网格
+            mesh, transform_info = ModelPreprocessor.normalize_mesh(mesh)
+            preprocessing_log.append(f"标准化网格: scale={transform_info['scale']:.4f}")
+            
+            return mesh, preprocessing_log
+            
+        except Exception as e:
+            error_msg = f"预处理失败: {str(e)}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
+    
+    def _generate_skeleton(self, mesh: trimesh.Trimesh, file_name: str, user_prompt: str) -> Dict[str, Any]:
+        """生成骨骼结构"""
+        try:
+            # 转换网格为点云
+            points_per_mesh = self.default_args['input_pc_num']
+            apply_marching_cubes = self.default_args['apply_marching_cubes']
+            octree_depth = self.default_args['octree_depth']
+            
+            point_clouds = MeshProcessor.convert_meshes_to_point_clouds(
+                [mesh], 
+                points_per_mesh, 
+                apply_marching_cubes, 
+                octree_depth
+            )
+            
+            pc_data = point_clouds[0]
+            
+            # 按照原始demo进行标准化处理
+            pc_coor = pc_data[:, :3]
+            normals = pc_data[:, 3:]
+            bounds = np.array([pc_coor.min(axis=0), pc_coor.max(axis=0)])
+            
+            # 存储变换信息以便后续去标准化
+            trans = (bounds[0] + bounds[1])[None, :] / 2
+            scale = ((bounds[1] - bounds[0]).max() + 1e-5)
+            
+            # 标准化坐标 - 与原始demo完全一致
+            pc_coor = pc_coor - (bounds[0] + bounds[1])[None, :] / 2
+            pc_coor = pc_coor / np.abs(pc_coor).max() * 0.9995
+            
+            # 组合坐标和法向量
+            pc_coor = pc_coor.astype(np.float32)
+            normals = normals.astype(np.float32)
+            pc_normal_data = np.concatenate([pc_coor, normals], axis=-1, dtype=np.float16)
+            
+            # 准备输入数据
+            pc_normal = torch.from_numpy(pc_normal_data).unsqueeze(0)
+            if self.device.type == "cuda":
+                pc_normal = pc_normal.cuda()
+            
+            # 获取mesh的变换信息
+            mesh_bounds = np.array([mesh.vertices.min(axis=0), mesh.vertices.max(axis=0)])
+            mesh_trans = (mesh_bounds[0] + mesh_bounds[1])[None, :] / 2
+            mesh_scale = ((mesh_bounds[1] - mesh_bounds[0]).max() + 1e-5)
+            
+            batch_data = {
+                'pc_normal': pc_normal,
+                'file_name': [os.path.splitext(file_name)[0]],
+                'trans': torch.from_numpy(mesh_trans).unsqueeze(0),
+                'scale': torch.tensor(mesh_scale).unsqueeze(0),
+                'vertices': torch.from_numpy(mesh.vertices).unsqueeze(0),
+                'faces': torch.from_numpy(mesh.faces).unsqueeze(0)
+            }
+            
+            # 生成骨骼
+            with torch.no_grad():
+                if self.accelerator:
+                    with self.accelerator.autocast():
+                        pred_bone_coords = self.model.generate(batch_data)
+                else:
+                    pred_bone_coords = self.model.generate(batch_data)
+            
+            # 处理��出 - 完全按照原始demo的流程
+            trans = batch_data['trans'][0].cpu().numpy()
+            scale = batch_data['scale'][0].cpu().numpy()
+            vertices = batch_data['vertices'][0].cpu().numpy()
+            faces = batch_data['faces'][0].cpu().numpy()
+            
+            skeleton = pred_bone_coords[0].cpu().numpy().squeeze()
+            pred_joints, pred_bones = pred_joints_and_bones(skeleton)
+            
+            # 去重处理
+            if self.default_args['hier_order']:
+                pred_joints, pred_bones, pred_root_index = remove_duplicate_joints(
+                    pred_joints, pred_bones, root_index=pred_bones[0][0]
+                )
+            else:
+                pred_joints, pred_bones = remove_duplicate_joints(pred_joints, pred_bones)
+                pred_root_index = 0
+            
+            # 重要：去标准化骨骼关节到原始模型坐标系
+            pred_joints_denorm = pred_joints * scale + trans
+            
+            return {
+                'joints': pred_joints_denorm.tolist(),  # 使用去标准化后的关节
+                'joints_normalized': pred_joints.tolist(),  # 保留标准化的关节用于可视化
+                'bones': pred_bones,
+                'root_index': pred_root_index,
+                'joint_count': len(pred_joints),
+                'bone_count': len(pred_bones),
+                'raw_skeleton': skeleton.tolist(),
+                'user_prompt': user_prompt,
+                'transform_info': {
+                    'trans': trans.tolist(),
+                    'scale': float(scale)
+                }
+            }
+            
+        except Exception as e:
+            error_msg = f"骨骼生成失败: {str(e)}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
+    
+    def _save_results(self, 
+                     skeleton_result: Dict[str, Any],
+                     mesh: trimesh.Trimesh,
+                     model_info: Dict[str, Any],
+                     session_dir: Path,
+                     user_prompt: str) -> Dict[str, str]:
+        """保存处理结果"""
+        try:
+            output_dir = session_dir / "outputs"
+            base_name = os.path.splitext(model_info['file_name'])[0]
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            
+            output_files = {}
+            
+            # 移除JSON格式输出以避免序列化问题
+            
+            # 保存骨骼OBJ - 使用去标准化后的关节
+            obj_file = output_dir / f"{base_name}_{timestamp}_skeleton.obj"
+            save_skeleton_obj(
+                np.array(skeleton_result['joints']),
+                skeleton_result['bones'],
+                str(obj_file),
+                skeleton_result.get('root_index', 0),
+                use_cone=self.default_args['hier_order']
+            )
+            output_files['skeleton_obj'] = str(obj_file)
+            
+            # 保存骨骼TXT
+            txt_file = output_dir / f"{base_name}_{timestamp}_rig.txt"
+            save_skeleton_to_txt(
+                np.array(skeleton_result['joints']),
+                skeleton_result['bones'],
+                skeleton_result.get('root_index', 0),
+                self.default_args['hier_order'],
+                mesh.vertices,
+                str(txt_file)
+            )
+            output_files['skeleton_txt'] = str(txt_file)
+            
+            # 保存处理后的网格
+            mesh_file = output_dir / f"{base_name}_{timestamp}_processed.obj"
+            mesh.export(str(mesh_file))
+            output_files['processed_mesh'] = str(mesh_file)
+            
+            # 保存处理报告（文本格式）
+            report_file = output_dir / f"{base_name}_{timestamp}_report.txt"
+            report_content = f"""MagicArticulate Processing Report
+=====================================
+
+File: {model_info['file_name']}
+Processing Time: {datetime.now().isoformat()}
+User Prompt: {user_prompt}
+
+Model Information:
+- Vertices: {model_info.get('vertex_count', 'N/A')}
+- Faces: {model_info.get('face_count', 'N/A')}
+- File Size: {model_info.get('file_size_mb', 'N/A')} MB
+- Format: {model_info.get('format', 'N/A')}
+
+Skeleton Results:
+- Joints: {skeleton_result.get('joint_count', 'N/A')}
+- Bones: {skeleton_result.get('bone_count', 'N/A')}
+- Root Index: {skeleton_result.get('root_index', 'N/A')}
+
+Generated Files:
+- Skeleton OBJ: {base_name}_{timestamp}_skeleton.obj
+- Skeleton TXT: {base_name}_{timestamp}_rig.txt
+- Processed Mesh: {base_name}_{timestamp}_processed.obj
+"""
+            
+            with open(report_file, 'w', encoding='utf-8') as f:
+                f.write(report_content)
+            output_files['report'] = str(report_file)
+            
+            logger.info(f"结果保存完成: {len(output_files)} 个文件")
+            return output_files
+            
+        except Exception as e:
+            error_msg = f"保存结果失败: {str(e)}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
+    
+    def _create_error_result(self, error_message: str, session_id: str, start_time: float) -> Dict[str, Any]:
+        """创建错误结果"""
+        processing_time = time.time() - start_time
+        
+        return {
+            'success': False,
+            'session_id': session_id,
+            'error': error_message,
+            'processing_time': processing_time,
+            'timestamp': datetime.now().isoformat()
+        }
+    
+    def _make_json_serializable(self, obj):
+        """将对象转换为JSON可序列化格式"""
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, dict):
+            return {key: self._make_json_serializable(value) for key, value in obj.items()}
+        elif isinstance(obj, list):
+            return [self._make_json_serializable(item) for item in obj]
+        else:
+            return obj
+    
+    def _create_args_object(self):
+        """创建参数对象"""
+        class Args:
+            def __init__(self, **kwargs):
+                for key, value in kwargs.items():
+                    setattr(self, key, value)
+        
+        return Args(**self.default_args)
+    
+    def get_session_info(self, session_id: str) -> Dict[str, Any]:
+        """获取会话信息"""
+        if session_id not in self.session_manager.sessions:
+            raise ValueError(f"会话不存在: {session_id}")
+        
+        return self.session_manager.sessions[session_id].copy()
+    
+    def list_user_sessions(self, user_id: Optional[str] = None) -> List[Dict[str, Any]]:
+        """列出用户会话"""
+        sessions = []
+        for session_id, session_info in self.session_manager.sessions.items():
+            if user_id is None or session_info.get('user_id') == user_id:
+                sessions.append({
+                    'session_id': session_id,
+                    **session_info
+                })
+        
+        return sorted(sessions, key=lambda x: x['created_at'], reverse=True)
+    
+    def cleanup_sessions(self, max_age_days: int = 7):
+        """清理旧会话"""
+        self.session_manager.cleanup_old_sessions(max_age_days)
+
+# 简化的使用接口
+def process_model_file(file_path: str, 
+                      user_prompt: str = "",
+                      model_weights_path: Optional[str] = None,
+                      output_dir: Optional[str] = None) -> Dict[str, Any]:
+    """
+    简化的模型处理接口
+    
+    Args:
+        file_path: 模型文件路径
+        user_prompt: 用户提示词
+        model_weights_path: 模型权重路径
+        output_dir: 输出目录
+        
+    Returns:
+        处理结果
+    """
+    api = MagicArticulateAPI(
+        model_weights_path=model_weights_path,
+        session_base_dir=output_dir or "temp_sessions"
+    )
+    
+    result = api.process_uploaded_model(
+        file_path=file_path,
+        user_prompt=user_prompt
+    )
+    
+    return result
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="MagicArticulate API 测试")
+    parser.add_argument("--input", required=True, help="输入模型文件路径")
+    parser.add_argument("--prompt", default="", help="用户提示词")
+    parser.add_argument("--weights", help="模型权重路径")
+    parser.add_argument("--output", default="api_outputs", help="输出目录")
+    
+    args = parser.parse_args()
+    
+    # 测试API
+    result = process_model_file(
+        file_path=args.input,
+        user_prompt=args.prompt,
+        model_weights_path=args.weights,
+        output_dir=args.output
+    )
+    
+    if result['success']:
+        print("✅ 处理成功!")
+        print(f"会话ID: {result['session_id']}")
+        print(f"处理时间: {result['processing_time']:.2f}秒")
+        print(f"关节数量: {result['skeleton_data']['joint_count']}")
+        print(f"骨骼数量: {result['skeleton_data']['bone_count']}")
+        print(f"输出文件: {len(result['output_files'])} 个")
+    else:
+        print("❌ 处理失败!")
+        print(f"错误: {result['error']}")

requirements.txt

@@ -0,0 +1,61 @@
+# HuggingFace Space Requirements - 修复版
+# 解决依赖冲突问题
+
+# HuggingFace Space必需
+gradio==5.36.2
+spaces
+huggingface-hub
+
+# 注意: HF Space会自动安装合适版本的PyTorch
+
+# 加速和Transformer
+accelerate==0.28.0
+transformers==4.39.3
+flash-attn>=2.3.0
+
+# 3D处理库 (核心功能)
+trimesh==4.2.3
+scikit-image==0.21.0
+
+# 科学计算
+numpy==1.26.4
+scipy==1.11.4
+
+# 图像处理
+Pillow==10.0.1
+matplotlib==3.7.2
+opencv-python==4.8.1.78
+
+# 3D相关
+imageio==2.31.5
+networkx==3.2.1
+
+# 工具库
+tqdm==4.66.1
+scikit-learn==1.3.2
+
+# Web功能 (用户上传支持)
+fastapi>=0.115.2
+pydantic>=2.5.0
+python-multipart>=0.0.9  # 修复: 使用与gradio兼容的版本
+aiofiles>=23.2.1
+
+# 文件和数据处理
+orjson==3.9.10
+python-dotenv==1.0.0
+pyyaml==6.0.1
+
+# 性能和监控
+psutil==5.9.6
+loguru==0.7.2
+
+# Michelangelo依赖
+omegaconf==2.3.0
+einops==0.6.0
+xatlas==0.0.7
+
+# MagicArticulate依赖
+mesh2sdf==1.1.0
+pyrender==0.1.45
+pytorch-lightning==1.9.3
+pythreejs==2.4.2

skeleton_models/__init__.py

@@ -0,0 +1,9 @@
+"""
+MagicArticulate Skeleton Models Package
+包含骨骼生成模型和形状优化功能
+"""
+
+from .skeletongen import SkeletonGPT
+from .shape_opt import ShapeOPTConfig
+
+__all__ = ['SkeletonGPT', 'ShapeOPTConfig']

skeleton_models/shape_opt.py

@@ -0,0 +1,406 @@
+# Modified from https://github.com/buaacyw/MeshAnything
+from transformers import AutoModelForCausalLM, AutoConfig, OPTConfig
+from transformers.models.opt.modeling_opt import OPTForCausalLM, OPTModel, OPTDecoder, OPTLearnedPositionalEmbedding, OPTDecoderLayer
+from typing import List, Optional, Tuple, Union
+from transformers.modeling_outputs import (
+    CausalLMOutputWithPast,
+)
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.utils import replace_return_docstrings
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+class ShapeOPTConfig(OPTConfig):
+    model_type = "shape_opt"
+
+class ShapeOPT(OPTForCausalLM):
+    config_class = ShapeOPTConfig
+    def __init__(self, config: ShapeOPTConfig):
+        super(OPTForCausalLM, self).__init__(config)
+        self.model = ShapeOPTModel(config)
+        self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class="OPTConfig")
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        bone_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+                provide it.
+
+                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
+                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
+
+                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, OPTForCausalLM
+
+        >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
+        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.decoder(
+            input_ids = input_ids,
+            bone_ids = bone_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        logits = self.lm_head(outputs[0]).contiguous()
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+class ShapeOPTModel(OPTModel):
+    config_class = ShapeOPTConfig
+    def __init__(self, config: ShapeOPTConfig):
+        super(OPTModel,self).__init__(config)
+        self.decoder = ShapeOPTDecoder(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+class ShapeOPTDecoder(OPTDecoder):
+    config_class = ShapeOPTConfig
+    def __init__(self, config: ShapeOPTConfig):
+        super(OPTDecoder,self).__init__(config)
+        self.config = config
+        self.dropout = config.dropout
+        self.layerdrop = config.layerdrop
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        assert config.word_embed_proj_dim == config.hidden_size
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
+        self.hidden_size = config.hidden_size
+        self.word_embed_proj_dim = config.word_embed_proj_dim
+        self.n_discrete_size = config.n_discrete_size
+                
+        self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
+        self.token_embed_positions = OPTBonePositionalEmbedding(config.bone_per_token+3, config.word_embed_proj_dim)
+
+        self.bone_per_token = config.bone_per_token
+        self.cond_length = config.cond_length
+        self.cond_embed = nn.Embedding(2, config.word_embed_proj_dim)
+        # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
+        # with checkpoints that have been fine-tuned before transformers v4.20.1
+        # see https://github.com/facebookresearch/metaseq/pull/164
+        if config.do_layer_norm_before and not config._remove_final_layer_norm:
+            self.final_layer_norm = nn.LayerNorm(
+                config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
+            )
+        else:
+            self.final_layer_norm = None
+
+        self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        bone_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        r"""
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+                provide it.
+
+                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+
+                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        # OPT Decoder
+        # print("used my Trans")
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # Transformer Decoder
+        if input_ids is not None and inputs_embeds is not None: # when training
+            pass
+        elif input_ids is not None: # when inference
+            assert not self.training
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+            inputs_embeds = self.embed_tokens(input_ids)
+            bone_embeds = self.token_embed_positions(attention_mask[:, self.cond_length:], bone_ids, input_ids,
+                                                     self.bone_per_token)
+            inputs_embeds += bone_embeds
+            cond_embed_query = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=inputs_embeds.device,
+                                            dtype=inputs_embeds.dtype).long()
+            inputs_embeds = inputs_embeds + self.cond_embed(cond_embed_query)
+
+        elif inputs_embeds is not None: # when generate first skeleton token
+            assert not self.training
+            total_length = inputs_embeds.shape[1]
+            cond_embed_query = torch.zeros((inputs_embeds.shape[0], total_length), device=inputs_embeds.device,
+                                            dtype=inputs_embeds.dtype).long()
+            inputs_embeds = inputs_embeds + self.cond_embed(cond_embed_query)
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        # embed positions
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            assert attention_mask is not None
+            causal_attention_mask = attention_mask if 0 in attention_mask else None
+        else:
+            raise ValueError("Only flash_attention_2 is supported")
+
+        pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
+
+        hidden_states = inputs_embeds + pos_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        # check if head_mask has a correct number of layers specified if desired
+        for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
+            if attn_mask is not None:
+                if attn_mask.size()[0] != (len(self.layers)):
+                    raise ValueError(
+                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+                        f" {head_mask.size()[0]}."
+                    )
+
+        for idx, decoder_layer in enumerate(self.layers):
+            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.training:
+                dropout_probability = torch.rand([])
+                if dropout_probability < self.layerdrop:
+                    continue
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_attention_mask,
+                    head_mask[idx] if head_mask is not None else None,
+                    None,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_attention_mask,
+                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        if self.final_layer_norm is not None:
+            hidden_states = self.final_layer_norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+class OPTBonePositionalEmbedding(nn.Embedding):
+    """
+    This module learns positional embeddings up to a fixed maximum size.
+    """
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        super().__init__(num_embeddings, embedding_dim)
+
+    def forward(self, attention_mask=None, bone_ids = None, input_ids = None, bone_per_token = None):
+        """`input_ids_shape` is expected to be [bsz x seqlen]."""
+        if bone_ids is not None:
+            return super().forward(bone_ids)
+
+        assert input_ids.shape[1] == 1
+        idx_in_extra = torch.isin(input_ids, torch.LongTensor([0, 1, 2]).to(input_ids.device))
+        cur_ids = input_ids.clone().detach()
+
+        cur_index = (attention_mask.sum(dim=1, keepdim=True) - 2) % bone_per_token + 3
+        cur_ids[~idx_in_extra]=cur_index[~idx_in_extra]
+
+        return super().forward(cur_ids)
+
+AutoConfig.register("shape_opt", ShapeOPTConfig)
+AutoModelForCausalLM.register(ShapeOPTConfig, ShapeOPT)
+

skeleton_models/skeletongen.py

@@ -0,0 +1,198 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import torch
+from torch import nn
+from transformers import AutoModelForCausalLM
+from third_party.Michelangelo.encode import load_model
+from skeleton_models.shape_opt import ShapeOPTConfig
+
+def undiscretize(t, low, high, num_discrete):
+    assert (t >= 0).all() and (t <= num_discrete-1).all()
+    assert high > low
+    t = t.float()
+    t /= num_discrete
+    t = t * (high - low) + low
+    assert (t < high).all() and (t >= low).all()
+    return t
+
+class SkeletonGPT(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.args = args
+        self.point_encoder = load_model()
+      
+        self.cond_length = 257
+        self.cond_dim = 768
+    
+        self.n_discrete_size = args.n_discrete_size
+
+        self.bone_per_token = 6  # (2 joints per bone)
+        self.max_length = int(args.n_max_bones * self.bone_per_token + 2 + self.cond_length)
+        self.pad_id = -1
+        
+        self.coor_continuous_range = (-0.5, 0.5)
+
+        vocab_size = self.n_discrete_size + 3 # 3 for bos, eos, pad
+        self.config = ShapeOPTConfig.from_pretrained(
+            args.llm,
+            n_positions=self.max_length,
+            max_position_embeddings=self.max_length,
+            vocab_size = vocab_size,
+            _attn_implementation="flash_attention_2"
+        )
+
+        self.bos_token_id = 0
+        self.eos_token_id = 1
+        self.pad_token_id = 2
+
+        self.config.bos_token_id = self.bos_token_id
+        self.config.eos_token_id = self.eos_token_id
+        self.config.pad_token_id = self.pad_token_id
+        self.config._attn_implementation ="flash_attention_2"
+        self.config.n_discrete_size = self.n_discrete_size
+        self.config.bone_per_token = self.bone_per_token
+        self.config.cond_length = self.cond_length
+
+        self.config.word_embed_proj_dim = self.config.hidden_size # 1024
+        
+       
+        self.transformer = AutoModelForCausalLM.from_config(
+            config=self.config, attn_implementation="flash_attention_2")
+        
+        self.cond_head_proj = nn.Linear(self.cond_dim, self.config.word_embed_proj_dim)
+        self.cond_proj = nn.Linear(self.cond_dim, self.config.word_embed_proj_dim)
+
+        self.eval()
+
+    def detokenize(self, input_ids):
+        # input_ids: torch.Tensor of shape (batch_size, seq_length)
+        batch_size = input_ids.size(0)
+
+        continuous_coors_list = []
+        num_bones_list = []
+        
+        for i in range(batch_size):
+            cur_ids = input_ids[i]  # Shape: (seq_length,)
+
+            # Remove padding tokens
+            cur_ids = cur_ids[cur_ids != self.pad_id]  # Shape: (effective_seq_length,)
+        
+            # Check if length is a multiple of 6 (2 joints * 3 coordinates)
+            if cur_ids.numel() % 6 != 0:
+                return None
+                # raise ValueError(f"Invalid length of input_ids in sample {i}. It should be a multiple of 6.")
+
+            num_bones = cur_ids.numel() // 6
+            num_bones_list.append(num_bones)
+
+            # Reshape into (num_bones, 6)
+            bone_coords = cur_ids.view(num_bones, 6)  # Shape: (num_bones, 6)
+
+            # Undiscretize the coordinates
+            # Initialize tensor to hold bone coordinates
+            bones_coors = torch.zeros((num_bones, 2, 3), dtype=torch.float16, device=cur_ids.device)
+
+            for j in range(num_bones):
+                bone_coord = bone_coords[j]  # Shape: (6,)
+
+                # Split into two joints
+                joint1_ids = bone_coord[:3]
+                joint2_ids = bone_coord[3:]
+
+                # Undiscretize joint coordinates
+                joint1_coords = undiscretize(joint1_ids, self.coor_continuous_range[0], self.coor_continuous_range[1], self.n_discrete_size)
+                joint2_coords = undiscretize(joint2_ids, self.coor_continuous_range[0], self.coor_continuous_range[1], self.n_discrete_size)
+
+                # Assign to bones_coors
+                bones_coors[j, 0, :] = joint1_coords
+                bones_coors[j, 1, :] = joint2_coords
+
+            continuous_coors_list.append(bones_coors)
+        
+        max_num_bones = max(num_bones_list)
+
+        # Initialize the continuous_coors tensor with NaNs
+        continuous_coors = torch.full(
+            (batch_size, max_num_bones, 2, 3),
+            float('nan'),
+            dtype=torch.float16,
+            device=input_ids.device
+        )
+
+        # Place the bones_coors into continuous_coors
+        for i in range(batch_size):
+            num_bones = num_bones_list[i]
+            continuous_coors[i, :num_bones, :, :] = continuous_coors_list[i]
+
+        return continuous_coors  # Shape: (batch_size, max_num_bones, 2, 3)
+
+
+    # def forward(self, data_dict: dict, is_eval: bool = False) -> dict:
+    #     return self.generate(data_dict)
+
+    def process_point_feature(self, point_feature):
+        
+        encode_feature = torch.zeros(self.args.batchsize_per_gpu, self.cond_length, self.config.word_embed_proj_dim,
+                                    device=self.cond_head_proj.weight.device, dtype=self.cond_head_proj.weight.dtype)
+        encode_feature[:, 0] = self.cond_head_proj(point_feature[:, 0])
+        shape_latents = self.point_encoder.to_shape_latents(point_feature[:, 1:])
+
+        encode_feature[:, 1:] = self.cond_proj(shape_latents)
+        
+        return encode_feature
+
+    @torch.no_grad()
+    def generate(self, data_dict) -> dict:
+
+        point_feature = self.point_encoder.encode_latents(data_dict["pc_normal"])
+        processed_point_feature = self.process_point_feature(point_feature=point_feature)
+        generate_length = self.max_length - self.cond_length
+        net_device = next(self.parameters()).device
+        outputs = torch.ones(self.args.batchsize_per_gpu, generate_length).long().to(net_device) * self.eos_token_id
+        # batch x ntokens
+        if self.args.num_beams is not None and "pc_normal" in data_dict:
+            results = self.transformer.generate(
+                inputs_embeds=processed_point_feature,
+                max_new_tokens=generate_length,  # all faces plus two
+                num_beams=self.args.num_beams,
+                bos_token_id=self.bos_token_id,
+                eos_token_id=self.eos_token_id,
+                pad_token_id=self.pad_token_id,
+            )
+        else:
+            results = self.transformer.generate(
+                inputs_embeds = processed_point_feature,
+                max_new_tokens = generate_length, # all faces plus two
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                bos_token_id = self.bos_token_id,
+                eos_token_id = self.eos_token_id,
+                pad_token_id = self.pad_token_id,
+            )
+        assert results.shape[1] <= generate_length # B x ID  bos is not included since it's predicted
+        outputs[:, :results.shape[1]] = results
+        # batch x ntokens ====> batch x ntokens x D
+        outputs = outputs[:, 1: -1] # eos and bos removed
+
+        outputs[outputs == self.bos_token_id] = self.pad_id
+        outputs[outputs == self.eos_token_id] = self.pad_id
+        outputs[outputs == self.pad_token_id] = self.pad_id
+
+        outputs[outputs != self.pad_id] -= 3
+        
+        gen_joints = self.detokenize(outputs)
+
+        return gen_joints

src/config.py

@@ -0,0 +1,90 @@
+"""
+配置文件
+"""
+
+import os
+from typing import Dict, Any
+
+# 文件限制
+MAX_FILE_SIZE_MB = 50
+SUPPORTED_FORMATS = ['.obj', '.glb', '.ply', '.stl']
+
+# 处理参数
+DEFAULT_PROCESSING_PARAMS = {
+    'input_pc_num': 8192,
+    'confidence_threshold': 0.8,
+    'generate_preview': True,
+    'timeout_seconds': 120,
+}
+
+# 演示提示模板
+DEMO_PROMPTS = {
+    'human': "realistic human skeleton for walking and animation",
+    'animal': "four-legged animal with spine and tail bones for natural movement",
+    'robot': "mechanical robot with joint articulation for industrial movements",
+    'bird': "bird skeleton with wing bones for flight animation",
+    'generic': "articulated skeleton suitable for animation"
+}
+
+# 示例模型描述
+EXAMPLE_MODELS = [
+    {
+        'name': 'Boy Character',
+        'file': 'boy.obj',
+        'prompt': DEMO_PROMPTS['human'],
+        'description': 'Human character suitable for walk cycle and basic animations'
+    },
+    {
+        'name': 'Dog Model',
+        'file': 'dog.obj', 
+        'prompt': DEMO_PROMPTS['animal'],
+        'description': 'Quadruped animal with natural bone structure'
+    },
+    {
+        'name': 'Bird Model',
+        'file': 'bird.obj',
+        'prompt': DEMO_PROMPTS['bird'],
+        'description': 'Bird with wing bones for flight animations'
+    },
+    {
+        'name': 'Robot/Mech',
+        'file': 'ironman.obj',
+        'prompt': DEMO_PROMPTS['robot'],
+        'description': 'Mechanical character with joint-based movement'
+    }
+]
+
+# UI配置
+UI_CONFIG = {
+    'title': '🎯 MagicArticulate MVP',
+    'description': """
+AI-powered 3D model articulation using skeletal generation. 
+Upload a 3D model and get an automatically generated skeleton for animation.
+    """,
+    'theme': 'soft',
+    'show_tips': True,
+    'max_examples': 4
+}
+
+# 性能配置
+PERFORMANCE_CONFIG = {
+    'use_gpu': True,
+    'mixed_precision': 'fp16',
+    'batch_size': 1,
+    'max_concurrent_requests': 2,
+    'cleanup_temp_files': True
+}
+
+def get_config() -> Dict[str, Any]:
+    """获取完整配置"""
+    return {
+        'file_limits': {
+            'max_size_mb': MAX_FILE_SIZE_MB,
+            'supported_formats': SUPPORTED_FORMATS
+        },
+        'processing': DEFAULT_PROCESSING_PARAMS,
+        'prompts': DEMO_PROMPTS,
+        'examples': EXAMPLE_MODELS,
+        'ui': UI_CONFIG,
+        'performance': PERFORMANCE_CONFIG
+    }

src/enhanced_magic_wrapper.py

@@ -0,0 +1,301 @@
+"""
+Enhanced MagicArticulate包装器
+集成MagicArticulate-Plus用户上传支持
+基于我们已完善的articulate_api.py
+"""
+
+import os
+import sys
+import time
+import logging
+import tempfile
+from pathlib import Path
+from typing import Optional, Dict, Any, Tuple, List
+
+# 添加必要的路径
+parent_dir = os.path.join(os.path.dirname(__file__), '..')
+sys.path.append(parent_dir)  # 添加mvp-space根目录
+sys.path.append(os.path.join(parent_dir, 'magic_articulate_plus'))
+
+# 详细的调试信息
+print(f"🔍 DEBUG: Current working directory: {os.getcwd()}")
+print(f"🔍 DEBUG: Script directory: {os.path.dirname(__file__)}")
+print(f"🔍 DEBUG: Parent directory: {parent_dir}")
+print(f"🔍 DEBUG: Python path includes:")
+for i, path in enumerate(sys.path):
+    print(f"  {i}: {path}")
+
+# 检查关键目录是否存在
+utils_dir = os.path.join(parent_dir, 'utils')
+magic_plus_dir = os.path.join(parent_dir, 'magic_articulate_plus')
+skeleton_dir = os.path.join(parent_dir, 'skeleton_models')
+
+print(f"🔍 DEBUG: Directory existence:")
+print(f"  utils directory exists: {os.path.exists(utils_dir)}")
+print(f"  magic_articulate_plus directory exists: {os.path.exists(magic_plus_dir)}")
+print(f"  skeleton_models directory exists: {os.path.exists(skeleton_dir)}")
+
+if os.path.exists(utils_dir):
+    print(f"🔍 DEBUG: utils directory contents: {os.listdir(utils_dir)}")
+if os.path.exists(magic_plus_dir):
+    print(f"🔍 DEBUG: magic_articulate_plus directory contents: {os.listdir(magic_plus_dir)}")
+
+# 导入我们已经完善的MagicArticulate-Plus功能
+try:
+    print("🔍 DEBUG: Attempting to import magic_articulate_plus.articulate_api...")
+    from magic_articulate_plus.articulate_api import (
+        MagicArticulateAPI, 
+        ModelValidator, 
+        process_model_file
+    )
+    print("✅ DEBUG: Successfully imported MagicArticulate-Plus components")
+    ENHANCED_AVAILABLE = True
+except ImportError as e:
+    print(f"❌ DEBUG: Import failed with error: {e}")
+    print(f"❌ DEBUG: Error type: {type(e)}")
+    import traceback
+    print(f"❌ DEBUG: Full traceback:")
+    traceback.print_exc()
+    logging.warning(f"MagicArticulate-Plus not available: {e}")
+    ENHANCED_AVAILABLE = False
+
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class EnhancedMagicWrapper:
+    """
+    增强版MagicArticulate包装器
+    支持用户上传任意3D模型文件
+    """
+    
+    def __init__(self, model_weights_path: Optional[str] = None):
+        # 如果没有指定权重路径，使用默认的空间模型（匹配demo.py hier_order=False）
+        if model_weights_path is None:
+            model_weights_path = "skeleton_ckpt/checkpoint_trainonv2_spatial.pth"
+        
+        self.model_weights_path = model_weights_path
+        self.initialized = False
+        
+        if ENHANCED_AVAILABLE:
+            # 使用我们完善的MagicArticulate-Plus API
+            self.api = MagicArticulateAPI(
+                model_weights_path=model_weights_path,
+                device="auto",
+                session_base_dir="hf_user_sessions"
+            )
+            logger.info(f"✅ 使用增强版MagicArticulate-Plus API (weights: {model_weights_path})")
+        else:
+            # 降级到原始包装器
+            logger.error("❌ MagicArticulate-Plus不可用，请检查集成")
+            self.api = None
+    
+    def initialize(self) -> bool:
+        """初始化API"""
+        try:
+            if not ENHANCED_AVAILABLE:
+                logger.error("增强版API不可用")
+                return False
+            
+            logger.info("🚀 初始化增强版MagicArticulate...")
+            
+            # 使用我们已经完善的初始化逻辑
+            success = self.api.initialize_model()
+            
+            if success:
+                self.initialized = True
+                logger.info("✅ 增强版MagicArticulate初始化成功")
+            else:
+                logger.error("❌ 增强版MagicArticulate初始化失败")
+            
+            return success
+            
+        except Exception as e:
+            logger.error(f"💥 初始化失败: {str(e)}")
+            return False
+    
+    def validate_uploaded_file(self, file_path: str) -> Tuple[bool, str, Dict[str, Any]]:
+        """
+        验证用户上传的文件
+        使用我们已完善的ModelValidator
+        """
+        try:
+            if not ENHANCED_AVAILABLE:
+                return False, "增强功能不可用", {}
+            
+            # 使用我们已经完善的验证逻辑
+            is_valid, error_msg, model_info = ModelValidator.validate_file(file_path)
+            
+            if is_valid:
+                logger.info(f"✅ 文件验证通过: {model_info.get('file_name', 'Unknown')}")
+            else:
+                logger.warning(f"⚠️ 文件验证失败: {error_msg}")
+            
+            return is_valid, error_msg, model_info
+            
+        except Exception as e:
+            error_msg = f"文件验证过程出错: {str(e)}"
+            logger.error(error_msg)
+            return False, error_msg, {}
+    
+    def process_3d_model(self, 
+                        model_file_path: str,
+                        prompt: str = "",
+                        confidence_threshold: float = 0.8,
+                        generate_preview: bool = True,
+                        **kwargs) -> Dict[str, Any]:
+        """
+        处理3D模型 - 支持用户上传
+        使用我们已完善的处理管道
+        """
+        try:
+            if not self.initialized:
+                return {
+                    'success': False,
+                    'error': 'API未初始化',
+                    'skeleton_data': None,
+                    'output_files': None,
+                    'processing_info': None
+                }
+            
+            if not ENHANCED_AVAILABLE:
+                return {
+                    'success': False,
+                    'error': '增强功能不可用',
+                    'skeleton_data': None,
+                    'output_files': None,
+                    'processing_info': None
+                }
+            
+            logger.info(f"🔄 开始处理用户上传的模型: {model_file_path}")
+            
+            # 首先验证文件
+            is_valid, error_msg, model_info = self.validate_uploaded_file(model_file_path)
+            if not is_valid:
+                return {
+                    'success': False,
+                    'error': f'文件验证失败: {error_msg}',
+                    'skeleton_data': None,
+                    'output_files': None,
+                    'processing_info': model_info
+                }
+            
+            # 准备处理选项
+            processing_options = {
+                'auto_repair': kwargs.get('auto_repair', True),
+                'target_faces': kwargs.get('target_faces', 10000),
+                'confidence_threshold': confidence_threshold,
+                'generate_preview': generate_preview
+            }
+            
+            # 使用我们已完善的处理API
+            result = self.api.process_uploaded_model(
+                file_path=model_file_path,
+                user_prompt=prompt,
+                processing_options=processing_options
+            )
+            
+            # 转换为MVP期望的格式
+            if result['success']:
+                logger.info("✅ 模型处理完成")
+                
+                # 添加处理信息
+                processing_info = {
+                    'input_file': model_info.get('file_name', 'Unknown'),
+                    'prompt': prompt,
+                    'joint_count': result['skeleton_data'].get('joint_count', 0),
+                    'bone_count': result['skeleton_data'].get('bone_count', 0),
+                    'confidence_threshold': confidence_threshold,
+                    'vertex_count': model_info.get('vertex_count', 0),
+                    'face_count': model_info.get('face_count', 0),
+                    'file_size_mb': model_info.get('file_size_mb', 0),
+                    'preprocessing_log': result.get('preprocessing_log', [])
+                }
+                
+                return {
+                    'success': True,
+                    'skeleton_data': result['skeleton_data'],
+                    'output_files': result['output_files'],
+                    'processing_info': processing_info
+                }
+            else:
+                logger.error(f"❌ 处理失败: {result.get('error', 'Unknown error')}")
+                return {
+                    'success': False,
+                    'error': result.get('error', 'Unknown error'),
+                    'skeleton_data': None,
+                    'output_files': None,
+                    'processing_info': None
+                }
+                
+        except Exception as e:
+            error_msg = f"处理过程中发生错误: {str(e)}"
+            logger.error(f"💥 {error_msg}")
+            
+            return {
+                'success': False,
+                'error': error_msg,
+                'skeleton_data': None,
+                'output_files': None,
+                'processing_info': None
+            }
+    
+    def get_supported_formats(self) -> List[str]:
+        """获取支持的文件格式"""
+        if ENHANCED_AVAILABLE:
+            # 返回我们已完善的格式列表
+            return list(ModelValidator.SUPPORTED_FORMATS)
+        else:
+            # 降级到基础格式
+            return ['.obj', '.glb', '.ply', '.stl']
+    
+    def get_session_info(self, session_id: str) -> Dict[str, Any]:
+        """获取会话信息"""
+        try:
+            if self.api and hasattr(self.api, 'get_session_info'):
+                return self.api.get_session_info(session_id)
+            else:
+                return {}
+        except Exception as e:
+            logger.error(f"获取会话信息��败: {str(e)}")
+            return {}
+    
+    def cleanup_sessions(self, max_age_days: int = 1):
+        """清理旧会话（HF Space内存限制）"""
+        try:
+            if self.api and hasattr(self.api, 'cleanup_sessions'):
+                self.api.cleanup_sessions(max_age_days)
+                logger.info(f"✅ 清理了超过 {max_age_days} 天的旧会话")
+        except Exception as e:
+            logger.error(f"清理会话失败: {str(e)}")
+
+# 为了保持兼容性，提供原始类名的别名
+MagicArticulateWrapper = EnhancedMagicWrapper
+
+# 简化的处理函数，直接使用我们完善的API
+def process_user_model(file_path: str, 
+                      prompt: str = "",
+                      model_weights_path: Optional[str] = None) -> Dict[str, Any]:
+    """
+    简化的用户模型处理接口
+    直接使用我们已完善的process_model_file函数
+    """
+    try:
+        if ENHANCED_AVAILABLE:
+            # 使用我们已完善的简化接口
+            return process_model_file(
+                file_path=file_path,
+                user_prompt=prompt,
+                model_weights_path=model_weights_path,
+                output_dir="hf_temp_sessions"
+            )
+        else:
+            return {
+                'success': False,
+                'error': 'Enhanced API not available'
+            }
+    except Exception as e:
+        return {
+            'success': False,
+            'error': f'Processing failed: {str(e)}'
+        }

src/utils.py

@@ -0,0 +1,290 @@
+"""
+工具函数
+"""
+
+import os
+import shutil
+import tempfile
+import logging
+from pathlib import Path
+from typing import Optional, Dict, Any, List, Tuple
+import numpy as np
+import trimesh
+
+logger = logging.getLogger(__name__)
+
+def validate_file(file_path: str, max_size_mb: int = 50) -> Tuple[bool, str]:
+    """
+    验证上传的文件
+    
+    Args:
+        file_path: 文件路径
+        max_size_mb: 最大文件大小（MB）
+    
+    Returns:
+        (是否有效, 错误信息)
+    """
+    try:
+        if not os.path.exists(file_path):
+            return False, "文件不存在"
+        
+        # 检查文件大小
+        file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
+        if file_size_mb > max_size_mb:
+            return False, f"文件太大: {file_size_mb:.1f}MB > {max_size_mb}MB"
+        
+        # 检查文件扩展名
+        file_ext = Path(file_path).suffix.lower()
+        supported_formats = ['.obj', '.glb', '.ply', '.stl']
+        if file_ext not in supported_formats:
+            return False, f"不支持的文件格式: {file_ext}"
+        
+        # 尝试加载文件
+        try:
+            mesh = trimesh.load(file_path, force='mesh')
+            if not hasattr(mesh, 'vertices') or len(mesh.vertices) == 0:
+                return False, "文件无法解析为有效的3D模型"
+        except Exception as e:
+            return False, f"文件格式错误: {str(e)}"
+        
+        return True, "文件有效"
+        
+    except Exception as e:
+        return False, f"文件验证失败: {str(e)}"
+
+def get_model_info(file_path: str) -> Dict[str, Any]:
+    """
+    获取模型信息
+    
+    Args:
+        file_path: 模型文件路径
+    
+    Returns:
+        模型信息字典
+    """
+    try:
+        mesh = trimesh.load(file_path, force='mesh')
+        
+        # 计算基本信息
+        vertex_count = len(mesh.vertices) if hasattr(mesh, 'vertices') else 0
+        face_count = len(mesh.faces) if hasattr(mesh, 'faces') else 0
+        
+        # 计算包围盒
+        if vertex_count > 0:
+            bounds = mesh.bounds
+            size = bounds[1] - bounds[0]
+            center = (bounds[0] + bounds[1]) / 2
+        else:
+            size = np.array([0, 0, 0])
+            center = np.array([0, 0, 0])
+        
+        # 计算表面积和体积
+        surface_area = mesh.area if hasattr(mesh, 'area') else 0
+        volume = mesh.volume if hasattr(mesh, 'volume') else 0
+        
+        return {
+            'file_name': os.path.basename(file_path),
+            'file_size_mb': os.path.getsize(file_path) / (1024 * 1024),
+            'vertex_count': vertex_count,
+            'face_count': face_count,
+            'bounding_box': {
+                'min': bounds[0].tolist() if vertex_count > 0 else [0, 0, 0],
+                'max': bounds[1].tolist() if vertex_count > 0 else [0, 0, 0],
+                'size': size.tolist(),
+                'center': center.tolist()
+            },
+            'surface_area': float(surface_area),
+            'volume': float(volume),
+            'is_watertight': mesh.is_watertight if hasattr(mesh, 'is_watertight') else False,
+            'is_closed': mesh.is_closed if hasattr(mesh, 'is_closed') else False
+        }
+        
+    except Exception as e:
+        logger.error(f"Failed to get model info: {str(e)}")
+        return {
+            'file_name': os.path.basename(file_path),
+            'error': str(e)
+        }
+
+def cleanup_temp_files(temp_dir: str, keep_files: Optional[List[str]] = None):
+    """
+    清理临时文件
+    
+    Args:
+        temp_dir: 临时目录
+        keep_files: 需要保留的文件列表
+    """
+    try:
+        if not os.path.exists(temp_dir):
+            return
+        
+        for file_name in os.listdir(temp_dir):
+            file_path = os.path.join(temp_dir, file_name)
+            
+            if keep_files and file_name in keep_files:
+                continue
+            
+            try:
+                if os.path.isfile(file_path):
+                    os.remove(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+            except Exception as e:
+                logger.warning(f"Failed to remove {file_path}: {str(e)}")
+                
+    except Exception as e:
+        logger.error(f"Cleanup failed: {str(e)}")
+
+def format_processing_time(seconds: float) -> str:
+    """
+    格式化处理时间
+    
+    Args:
+        seconds: 秒数
+    
+    Returns:
+        格式化的时间字符串
+    """
+    if seconds < 60:
+        return f"{seconds:.1f}秒"
+    elif seconds < 3600:
+        minutes = seconds / 60
+        return f"{minutes:.1f}分钟"
+    else:
+        hours = seconds / 3600
+        return f"{hours:.1f}小时"
+
+def get_prompt_suggestions(model_info: Dict[str, Any]) -> List[str]:
+    """
+    根据模型信息获取提示建议
+    
+    Args:
+        model_info: 模型信息
+    
+    Returns:
+        提示建议列表
+    """
+    suggestions = []
+    
+    # 基于文件名的建议
+    file_name = model_info.get('file_name', '').lower()
+    
+    if any(keyword in file_name for keyword in ['human', 'person', 'character', 'boy', 'girl']):
+        suggestions.extend([
+            "realistic human skeleton for walking animations",
+            "character with full body rig for game animation",
+            "human bone structure suitable for motion capture"
+        ])
+    elif any(keyword in file_name for keyword in ['dog', 'cat', 'animal', 'pet']):
+        suggestions.extend([
+            "four-legged animal with spine and tail bones",
+            "quadruped skeleton for natural movement",
+            "animal bone structure with flexible spine"
+        ])
+    elif any(keyword in file_name for keyword in ['bird', 'eagle', 'chicken']):
+        suggestions.extend([
+            "bird skeleton with wing bones for flight",
+            "avian bone structure with hollow bones",
+            "bird with articulated wings and tail"
+        ])
+    elif any(keyword in file_name for keyword in ['robot', 'mech', 'mechanical']):
+        suggestions.extend([
+            "mechanical robot with joint articulation",
+            "industrial robot with precise joint control",
+            "mech suit with hydraulic joint system"
+        ])
+    else:
+        suggestions.extend([
+            "articulated skeleton suitable for animation",
+            "flexible bone structure for general movement",
+            "skeleton with natural joint hierarchy"
+        ])
+    
+    # 基于模型复杂度的建议
+    vertex_count = model_info.get('vertex_count', 0)
+    if vertex_count > 10000:
+        suggestions.append("detailed skeleton for high-poly model")
+    elif vertex_count < 1000:
+        suggestions.append("simple skeleton for low-poly model")
+    
+    return suggestions[:5]  # 限制建议数量
+
+def create_processing_status(stage: str, progress: float, message: str) -> Dict[str, Any]:
+    """
+    创建处理状态信息
+    
+    Args:
+        stage: 处理阶段
+        progress: 进度 (0-1)
+        message: 状态消息
+    
+    Returns:
+        状态信息字典
+    """
+    return {
+        'stage': stage,
+        'progress': min(max(progress, 0.0), 1.0),
+        'message': message,
+        'timestamp': __import__('time').time()
+    }
+
+def estimate_processing_time(model_info: Dict[str, Any]) -> float:
+    """
+    估算处理时间
+    
+    Args:
+        model_info: 模型信息
+    
+    Returns:
+        估算的处理时间（秒）
+    """
+    try:
+        vertex_count = model_info.get('vertex_count', 1000)
+        face_count = model_info.get('face_count', 1000)
+        
+        # 基于模型复杂度的简单估算
+        complexity_factor = (vertex_count + face_count) / 10000
+        base_time = 30  # 基础处理时间30秒
+        
+        estimated_time = base_time * (1 + complexity_factor * 0.5)
+        return min(estimated_time, 120)  # 最多120秒
+        
+    except Exception:
+        return 60  # 默认60秒
+
+def generate_download_filename(original_name: str, suffix: str) -> str:
+    """
+    生成下载文件名
+    
+    Args:
+        original_name: 原始文件名
+        suffix: 后缀
+    
+    Returns:
+        新文件名
+    """
+    base_name = os.path.splitext(original_name)[0]
+    return f"{base_name}_{suffix}"
+
+def safe_json_serialize(obj: Any) -> Any:
+    """
+    安全的JSON序列化
+    
+    Args:
+        obj: 要序列化的对象
+    
+    Returns:
+        可序列化的对象
+    """
+    if isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, np.floating):
+        return float(obj)
+    elif isinstance(obj, np.integer):
+        return int(obj)
+    elif isinstance(obj, dict):
+        return {k: safe_json_serialize(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [safe_json_serialize(item) for item in obj]
+    else:
+        return obj

third_party/Michelangelo/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
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+  7. Additional Terms.
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+  "Additional permissions" are terms that supplement the terms of this
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+  9. Acceptance Not Required for Having Copies.
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+  10. Automatic Licensing of Downstream Recipients.
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+  11. Patents.
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+  A "contributor" is a copyright holder who authorizes use under this
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+  A patent license is "discriminatory" if it does not include within
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+
+  Nothing in this License shall be construed as excluding or limiting
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+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
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+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
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+
+  14. Revised Versions of this License.
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+  The Free Software Foundation may publish revised and/or new versions of
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+  If the Program specifies that a proxy can decide which future
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+  17. Interpretation of Sections 15 and 16.
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+  If the disclaimer of warranty and limitation of liability provided
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
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+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
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+    This program is free software: you can redistribute it and/or modify
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+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
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+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
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+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
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+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

third_party/Michelangelo/README.md

@@ -0,0 +1,113 @@
+# Michelangelo
+
+## [Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation](https://neuralcarver.github.io/michelangelo)<br/>
+[Zibo Zhao](https://github.com/Maikouuu),
+[Wen Liu](https://github.com/StevenLiuWen),
+[Xin Chen](https://chenxin.tech/),
+[Xianfang Zeng](https://github.com/Zzlongjuanfeng),
+[Rui Wang](https://wrong.wang/),
+[Pei Cheng](https://neuralcarver.github.io/michelangelo),
+[Bin Fu](https://neuralcarver.github.io/michelangelo),
+[Tao Chen](https://eetchen.github.io),
+[Gang Yu](https://www.skicyyu.org),
+[Shenghua Gao](https://sist.shanghaitech.edu.cn/sist_en/2020/0814/c7582a54772/page.htm)<br/>
+### [Hugging Face Demo](https://huggingface.co/spaces/Maikou/Michelangelo) | [Project Page](https://neuralcarver.github.io/michelangelo/) | [Arxiv](https://arxiv.org/abs/2306.17115) | [Paper](https://openreview.net/pdf?id=xmxgMij3LY)<br/>
+
+https://github.com/NeuralCarver/Michelangelo/assets/37449470/123bae2c-fbb1-4d63-bd13-0e300a550868
+
+Visualization of the 3D shape produced by our framework, which splits into triplets with a conditional input on the left, a normal map in the middle, and a triangle mesh on the right. The generated 3D shapes semantically conform to the visual or textural conditional inputs.<br/>
+
+## 🔆 Features
+**Michelangelo** possesses three capabilities: 
+
+1. Representing a shape into shape-image-text aligned space;
+2. Image-conditioned Shape Generation;
+3. Text-conditioned Shape Generation.
+
+<details>
+  <summary><b> Techniques </b></summary>
+
+We present a novel _alignment-before-generation_ approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional generative model from images or texts to 3D shapes is prone to producing inconsistent results with the conditions because 3D shapes have an additional dimension whose distribution significantly differs from that of 2D images and texts. To bridge the domain gap among the three modalities and facilitate multi-modal-conditioned 3D shape generation, we explore representing 3D shapes in a shape-image-text-aligned space. Our framework comprises two models: a Shape-Image-Text-Aligned Variational Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model (ASLDM). The former model encodes the 3D shapes into the shape latent space aligned to the image and text and reconstructs the fine-grained 3D neural fields corresponding to given shape embeddings via the transformer-based decoder. The latter model learns a probabilistic mapping function from the image or text space to the latent shape space. Our extensive experiments demonstrate that our proposed approach can generate higher-quality and more diverse 3D shapes that better semantically conform to the visual or textural conditional inputs, validating the effectiveness of the shape-image-text-aligned space for cross-modality 3D shape generation.
+
+![newnetwork](https://github.com/NeuralCarver/Michelangelo/assets/16475892/d5231fb7-7768-45ee-92e1-3599a4c43a2c)
+</details>
+
+## 📰 News
+- [2024/1/23] Set up the <a href="https://huggingface.co/spaces/Maikou/Michelangelo">Hugging Face Demo</a> and release the code
+- [2023/09/22] **Michelangelo got accepted by NeurIPS 2023!**
+- [2023/6/29] Upload paper and init project
+
+## ⚙️ Setup
+
+### Installation
+Follow the command below to install the environment. We have tested the installation package on Tesla V100 and Tesla T4. 
+```
+git clone https://github.com/NeuralCarver/Michelangelo.git
+cd Michelangelo
+conda create --name Michelangelo python=3.9
+conda activate Michelangelo 
+pip install -r requirements.txt
+```
+
+### Checkpoints
+Pleasae download weights from <a href="https://huggingface.co/Maikou/Michelangelo/tree/main/checkpoints">Hugging Face Model Space</a> and put it to root folder. We have also uploaded the weights related to CLIP to facilitate quick usage.
+
+<details>
+  <summary><b>  
+    Tips for debugging configureation
+  </b></summary>
+
+- If something goes wrong in the environment configuration process unfortunately, the user may consider skipping those packages, such as pysdf, torch-cluster, and torch-scatter. These packages will not affect the execution of the commands we provide.
+- If you encounter any issues while downloading CLIP, you can consider downloading it from [CLIP's Hugging Face page](https://huggingface.co/openai/clip-vit-large-patch14). Once the download is complete, remember to modify line [26](https://github.com/NeuralCarver/Michelangelo/blob/b53fa004cd4aeb0f4eb4d159ecec8489a4450dab/configs/text_cond_diffuser_asl/text-ASLDM-256.yaml#L26C1-L26C76) and line [34](https://github.com/NeuralCarver/Michelangelo/blob/b53fa004cd4aeb0f4eb4d159ecec8489a4450dab/configs/text_cond_diffuser_asl/text-ASLDM-256.yaml#L34) in the config file for providing correct path of CLIP.
+- From [issue 6](https://github.com/NeuralCarver/Michelangelo/issues/6#issuecomment-1913513382). For Windows users, running wsl2 + ubuntu 22.04, will have issues. As discussed in [issue 786](https://github.com/microsoft/WSL/issues/8587) it is just a matter to add this in the .bashrc:
+```
+export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH.
+```
+</details>
+
+## ⚡ Quick Start
+
+### Inference
+
+#### Reconstruction a 3D shape
+```
+./scripts/inference/reconstruction.sh
+```
+
+#### Image-conditioned shape generation
+```
+./scripts/inference/image2mesh.sh
+```
+
+#### Text-conditioned shape generation
+```
+./scripts/inference/text2mesh.sh
+```
+
+#### Simply run all the scripts
+```
+./scripts/infer.sh
+```
+
+
+## ❓ FAQ
+
+## Citation
+
+If you find our code or paper helps, please consider citing:
+
+```bibtex
+@inproceedings{
+zhao2023michelangelo,
+title={Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation},
+author={Zibo Zhao and Wen Liu and Xin Chen and Xianfang Zeng and Rui Wang and Pei Cheng and BIN FU and Tao Chen and Gang YU and Shenghua Gao},
+booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+year={2023},
+url={https://openreview.net/forum?id=xmxgMij3LY}
+}
+```
+
+## License
+
+This code is distributed under an [GPL-3.0 license](LICENSE).
+

third_party/Michelangelo/configs/shapevae-256.yaml

@@ -0,0 +1,46 @@
+model:
+  target: third_party.Michelangelo.michelangelo.models.tsal.asl_pl_module.AlignedShapeAsLatentPLModule
+  params:
+    shape_module_cfg:
+      target: third_party.Michelangelo.michelangelo.models.tsal.sal_perceiver.AlignedShapeLatentPerceiver
+      params:
+        num_latents: 256
+        embed_dim: 64
+        point_feats: 3   # normal
+        num_freqs: 8
+        include_pi: false
+        heads: 12
+        width: 768
+        num_encoder_layers: 8
+        num_decoder_layers: 16
+        use_ln_post: true
+        init_scale: 0.25
+        qkv_bias: false
+        use_checkpoint: true
+    aligned_module_cfg:
+      target: third_party.Michelangelo.michelangelo.models.tsal.clip_asl_module.CLIPAlignedShapeAsLatentModule
+      params:
+        clip_model_version: "./checkpoints/clip/clip-vit-large-patch14"
+
+    loss_cfg:
+      target: third_party.Michelangelo.michelangelo.models.tsal.loss.ContrastKLNearFar
+      params:
+        contrast_weight: 0.1
+        near_weight: 0.1
+        kl_weight: 0.001
+
+    optimizer_cfg:
+      optimizer:
+        target: torch.optim.AdamW
+        params:
+          betas: [0.9, 0.99]
+          eps: 1.e-6
+          weight_decay: 1.e-2
+
+      scheduler:
+        target: third_party.Michelangelo.michelangelo.utils.trainings.lr_scheduler.LambdaWarmUpCosineFactorScheduler
+        params:
+          warm_up_steps: 5000
+          f_start: 1.e-6
+          f_min: 1.e-3
+          f_max: 1.0

third_party/Michelangelo/encode.py

@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+import os
+import argparse
+from omegaconf import OmegaConf, DictConfig, ListConfig
+import numpy as np
+import torch
+from .michelangelo.utils.misc import instantiate_from_config
+
+def load_surface(fp):
+    
+    with np.load(fp) as input_pc:
+        surface = input_pc['points']
+        normal = input_pc['normals']
+    
+    rng = np.random.default_rng()
+    ind = rng.choice(surface.shape[0], 4096, replace=False)
+    surface = torch.FloatTensor(surface[ind])
+    normal = torch.FloatTensor(normal[ind])
+    
+    surface = torch.cat([surface, normal], dim=-1).unsqueeze(0).cuda()
+    
+    return surface
+
+def reconstruction(args, model, bounds=(-1.25, -1.25, -1.25, 1.25, 1.25, 1.25), octree_depth=7, num_chunks=10000):
+
+    surface = load_surface(args.pointcloud_path)
+    # old_surface = surface.clone()
+
+    # surface[0,:,0]*=-1
+    # surface[0,:,1]*=-1
+    surface[0,:,2]*=-1
+
+    # encoding
+    shape_embed, shape_latents = model.model.encode_shape_embed(surface, return_latents=True)    
+    shape_zq, posterior = model.model.shape_model.encode_kl_embed(shape_latents)
+
+    # decoding
+    latents = model.model.shape_model.decode(shape_zq)
+    # geometric_func = partial(model.model.shape_model.query_geometry, latents=latents)
+    
+    return 0
+
+def load_model(ckpt_path="third_party/Michelangelo/checkpoints/aligned_shape_latents/shapevae-256.ckpt"):
+    import urllib.request
+    from pathlib import Path
+    
+    # 自动下载checkpoint文件如果不存在
+    if not os.path.exists(ckpt_path):
+        print(f"Downloading checkpoint to {ckpt_path}...")
+        os.makedirs(os.path.dirname(ckpt_path), exist_ok=True)
+        
+        # HuggingFace直接下载链接
+        download_url = "https://huggingface.co/Maikou/Michelangelo/resolve/main/checkpoints/aligned_shape_latents/shapevae-256.ckpt"
+        
+        try:
+            print("正在从HuggingFace下载模型文件...")
+            urllib.request.urlretrieve(download_url, ckpt_path)
+            print(f"✅ 模型文件下载完成: {ckpt_path}")
+        except Exception as e:
+            print(f"❌ 模型文件下载失败: {e}")
+            # 如果下载失败，返回一个简化的模型
+            import torch.nn as nn
+            class DummyModel(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.dummy = nn.Linear(1, 1)
+                def forward(self, x):
+                    return x
+                def encode(self, x):
+                    return torch.randn(1, 768)  # 返回期望的特征维度
+            print("⚠️ 使用简化模型替代")
+            return DummyModel()
+    
+    model_config = OmegaConf.load("third_party/Michelangelo/configs/shapevae-256.yaml")
+    if hasattr(model_config, "model"):
+        model_config = model_config.model
+
+    model = instantiate_from_config(model_config, ckpt_path=ckpt_path)
+
+    return model
+if __name__ == "__main__":
+    '''
+    1. Reconstruct point cloud
+    2. Image-conditioned generation
+    3. Text-conditioned generation
+    '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config_path", type=str, required=True)
+    parser.add_argument("--ckpt_path", type=str, required=True)
+    parser.add_argument("--pointcloud_path", type=str, default='./example_data/surface.npz', help='Path to the input point cloud')
+    parser.add_argument("--image_path", type=str, help='Path to the input image')
+    parser.add_argument("--text", type=str, help='Input text within a format: A 3D model of motorcar; Porsche 911.')
+    parser.add_argument("--output_dir", type=str, default='./output')
+    parser.add_argument("-s", "--seed", type=int, default=0)
+    args = parser.parse_args()
+    
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Output directory: {args.output_dir}')
+    print(f'-----------------------------------------------------------------------------')
+    
+    reconstruction(args, load_model(args))

third_party/Michelangelo/inference.py

@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+import os
+import time
+from collections import OrderedDict
+from typing import Optional, List
+import argparse
+from functools import partial
+
+from einops import repeat, rearrange
+import numpy as np
+from PIL import Image
+import trimesh
+import cv2
+
+import torch
+import pytorch_lightning as pl
+
+from michelangelo.models.tsal.tsal_base import Latent2MeshOutput
+from michelangelo.models.tsal.inference_utils import extract_geometry
+from michelangelo.utils.misc import get_config_from_file, instantiate_from_config
+from michelangelo.utils.visualizers.pythreejs_viewer import PyThreeJSViewer
+from michelangelo.utils.visualizers import html_util
+
+def load_model(args):
+
+    model_config = get_config_from_file(args.config_path)
+    if hasattr(model_config, "model"):
+        model_config = model_config.model
+
+    model = instantiate_from_config(model_config, ckpt_path=args.ckpt_path)
+    model = model.cuda()
+    model = model.eval()
+
+    return model
+
+def load_surface(fp):
+    
+    with np.load(args.pointcloud_path) as input_pc:
+        surface = input_pc['points']
+        normal = input_pc['normals']
+    
+    rng = np.random.default_rng()
+    ind = rng.choice(surface.shape[0], 4096, replace=False)
+    surface = torch.FloatTensor(surface[ind])
+    normal = torch.FloatTensor(normal[ind])
+    
+    surface = torch.cat([surface, normal], dim=-1).unsqueeze(0).cuda()
+    
+    return surface
+
+def prepare_image(args, number_samples=2):
+    
+    image = cv2.imread(f"{args.image_path}")
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    image_pt = torch.tensor(image).float()
+    image_pt = image_pt / 255 * 2 - 1
+    image_pt = rearrange(image_pt, "h w c -> c h w")
+    
+    image_pt = repeat(image_pt, "c h w -> b c h w", b=number_samples)
+
+    return image_pt
+
+def save_output(args, mesh_outputs):
+    
+    os.makedirs(args.output_dir, exist_ok=True)
+    for i, mesh in enumerate(mesh_outputs):
+        mesh.mesh_f = mesh.mesh_f[:, ::-1]
+        mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f)
+
+        name = str(i) + "_out_mesh.obj"
+        mesh_output.export(os.path.join(args.output_dir, name), include_normals=True)
+
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Finished and mesh saved in {args.output_dir}')
+    print(f'-----------------------------------------------------------------------------')        
+
+    return 0
+
+def reconstruction(args, model, bounds=(-1.25, -1.25, -1.25, 1.25, 1.25, 1.25), octree_depth=7, num_chunks=10000):
+
+    surface = load_surface(args.pointcloud_path)
+    
+    # encoding
+    shape_embed, shape_latents = model.model.encode_shape_embed(surface, return_latents=True)    
+    shape_zq, posterior = model.model.shape_model.encode_kl_embed(shape_latents)
+
+    # decoding
+    latents = model.model.shape_model.decode(shape_zq)
+    geometric_func = partial(model.model.shape_model.query_geometry, latents=latents)
+    
+    # reconstruction
+    mesh_v_f, has_surface = extract_geometry(
+        geometric_func=geometric_func,
+        device=surface.device,
+        batch_size=surface.shape[0],
+        bounds=bounds,
+        octree_depth=octree_depth,
+        num_chunks=num_chunks,
+    )
+    recon_mesh = trimesh.Trimesh(mesh_v_f[0][0], mesh_v_f[0][1])
+    
+    # save
+    os.makedirs(args.output_dir, exist_ok=True)
+    recon_mesh.export(os.path.join(args.output_dir, 'reconstruction.obj'))    
+    
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Finished and mesh saved in {os.path.join(args.output_dir, "reconstruction.obj")}')
+    print(f'-----------------------------------------------------------------------------')
+    
+    return 0
+
+def image2mesh(args, model, guidance_scale=7.5, box_v=1.1, octree_depth=7):
+
+    sample_inputs = {
+        "image": prepare_image(args)
+    }
+    
+    mesh_outputs = model.sample(
+        sample_inputs,
+        sample_times=1,
+        guidance_scale=guidance_scale,
+        return_intermediates=False,
+        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
+        octree_depth=octree_depth,
+    )[0]
+    
+    save_output(args, mesh_outputs)
+    
+    return 0
+
+def text2mesh(args, model, num_samples=2, guidance_scale=7.5, box_v=1.1, octree_depth=7):
+
+    sample_inputs = {
+        "text": [args.text] * num_samples
+    }
+    mesh_outputs = model.sample(
+        sample_inputs,
+        sample_times=1,
+        guidance_scale=guidance_scale,
+        return_intermediates=False,
+        bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v],
+        octree_depth=octree_depth,
+    )[0]
+    
+    save_output(args, mesh_outputs)
+    
+    return 0
+
+task_dick = {
+    'reconstruction': reconstruction,
+    'image2mesh': image2mesh,
+    'text2mesh': text2mesh,
+}
+
+if __name__ == "__main__":
+    '''
+    1. Reconstruct point cloud
+    2. Image-conditioned generation
+    3. Text-conditioned generation
+    '''
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--task", type=str, choices=['reconstruction', 'image2mesh', 'text2mesh'], required=True)
+    parser.add_argument("--config_path", type=str, required=True)
+    parser.add_argument("--ckpt_path", type=str, required=True)
+    parser.add_argument("--pointcloud_path", type=str, default='./example_data/surface.npz', help='Path to the input point cloud')
+    parser.add_argument("--image_path", type=str, help='Path to the input image')
+    parser.add_argument("--text", type=str, help='Input text within a format: A 3D model of motorcar; Porsche 911.')
+    parser.add_argument("--output_dir", type=str, default='./output')
+    parser.add_argument("-s", "--seed", type=int, default=0)
+    args = parser.parse_args()
+    
+    pl.seed_everything(args.seed)
+
+    print(f'-----------------------------------------------------------------------------')
+    print(f'>>> Running {args.task}')
+    args.output_dir = os.path.join(args.output_dir, args.task)
+    print(f'>>> Output directory: {args.output_dir}')
+    print(f'-----------------------------------------------------------------------------')
+    
+    task_dick[args.task](args, load_model(args))

third_party/Michelangelo/michelangelo/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

third_party/Michelangelo/michelangelo/data/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

third_party/Michelangelo/michelangelo/data/templates.json

@@ -0,0 +1,69 @@
+{
+    "shape": [
+        "a point cloud model of {}.",
+        "There is a {} in the scene.",
+        "There is the {} in the scene.",
+        "a photo of a {} in the scene.",
+        "a photo of the {} in the scene.",
+        "a photo of one {} in the scene.",
+        "itap of a {}.",
+        "itap of my {}.",
+        "itap of the {}.",
+        "a photo of a {}.",
+        "a photo of my {}.",
+        "a photo of the {}.",
+        "a photo of one {}.",
+        "a photo of many {}.",
+        "a good photo of a {}.",
+        "a good photo of the {}.",
+        "a bad photo of a {}.",
+        "a bad photo of the {}.",
+        "a photo of a nice {}.",
+        "a photo of the nice {}.",
+        "a photo of a cool {}.",
+        "a photo of the cool {}.",
+        "a photo of a weird {}.",
+        "a photo of the weird {}.",
+        "a photo of a small {}.",
+        "a photo of the small {}.",
+        "a photo of a large {}.",
+        "a photo of the large {}.",
+        "a photo of a clean {}.",
+        "a photo of the clean {}.",
+        "a photo of a dirty {}.",
+        "a photo of the dirty {}.",
+        "a bright photo of a {}.",
+        "a bright photo of the {}.",
+        "a dark photo of a {}.",
+        "a dark photo of the {}.",
+        "a photo of a hard to see {}.",
+        "a photo of the hard to see {}.",
+        "a low resolution photo of a {}.",
+        "a low resolution photo of the {}.",
+        "a cropped photo of a {}.",
+        "a cropped photo of the {}.",
+        "a close-up photo of a {}.",
+        "a close-up photo of the {}.",
+        "a jpeg corrupted photo of a {}.",
+        "a jpeg corrupted photo of the {}.",
+        "a blurry photo of a {}.",
+        "a blurry photo of the {}.",
+        "a pixelated photo of a {}.",
+        "a pixelated photo of the {}.",
+        "a black and white photo of the {}.",
+        "a black and white photo of a {}",
+        "a plastic {}.",
+        "the plastic {}.",
+        "a toy {}.",
+        "the toy {}.",
+        "a plushie {}.",
+        "the plushie {}.",
+        "a cartoon {}.",
+        "the cartoon {}.",
+        "an embroidered {}.",
+        "the embroidered {}.",
+        "a painting of the {}.",
+        "a painting of a {}."
+    ]
+
+}

third_party/Michelangelo/michelangelo/data/transforms.py

@@ -0,0 +1,407 @@
+# -*- coding: utf-8 -*-
+import os
+import time
+import numpy as np
+import warnings
+import random
+from omegaconf.listconfig import ListConfig
+from webdataset import pipelinefilter
+import torch
+import torchvision.transforms.functional as TVF
+from torchvision.transforms import InterpolationMode
+from torchvision.transforms.transforms import _interpolation_modes_from_int
+from typing import Sequence
+
+from third_party.miche.michelangelo.utils import instantiate_from_config
+
+
+def _uid_buffer_pick(buf_dict, rng):
+    uid_keys = list(buf_dict.keys())
+    selected_uid = rng.choice(uid_keys)
+    buf = buf_dict[selected_uid]
+
+    k = rng.randint(0, len(buf) - 1)
+    sample = buf[k]
+    buf[k] = buf[-1]
+    buf.pop()
+
+    if len(buf) == 0:
+        del buf_dict[selected_uid]
+
+    return sample
+
+
+def _add_to_buf_dict(buf_dict, sample):
+    key = sample["__key__"]
+    uid, uid_sample_id = key.split("_")
+    if uid not in buf_dict:
+        buf_dict[uid] = []
+    buf_dict[uid].append(sample)
+
+    return buf_dict
+
+
+def _uid_shuffle(data, bufsize=1000, initial=100, rng=None, handler=None):
+    """Shuffle the data in the stream.
+
+    This uses a buffer of size `bufsize`. Shuffling at
+    startup is less random; this is traded off against
+    yielding samples quickly.
+
+    data: iterator
+    bufsize: buffer size for shuffling
+    returns: iterator
+    rng: either random module or random.Random instance
+
+    """
+    if rng is None:
+        rng = random.Random(int((os.getpid() + time.time()) * 1e9))
+    initial = min(initial, bufsize)
+    buf_dict = dict()
+    current_samples = 0
+    for sample in data:
+        _add_to_buf_dict(buf_dict, sample)
+        current_samples += 1
+
+        if current_samples < bufsize:
+            try:
+                _add_to_buf_dict(buf_dict, next(data))  # skipcq: PYL-R1708
+                current_samples += 1
+            except StopIteration:
+                pass
+
+        if current_samples >= initial:
+            current_samples -= 1
+            yield _uid_buffer_pick(buf_dict, rng)
+
+    while current_samples > 0:
+        current_samples -= 1
+        yield _uid_buffer_pick(buf_dict, rng)
+
+
+uid_shuffle = pipelinefilter(_uid_shuffle)
+
+
+class RandomSample(object):
+    def __init__(self,
+                 num_volume_samples: int = 1024,
+                 num_near_samples: int = 1024):
+
+        super().__init__()
+
+        self.num_volume_samples = num_volume_samples
+        self.num_near_samples = num_near_samples
+
+    def __call__(self, sample):
+        rng = np.random.default_rng()
+
+        # 1. sample surface input
+        total_surface = sample["surface"]
+        ind = rng.choice(total_surface.shape[0], replace=False)
+        surface = total_surface[ind]
+
+        # 2. sample volume/near geometric points
+        vol_points = sample["vol_points"]
+        vol_label = sample["vol_label"]
+        near_points = sample["near_points"]
+        near_label = sample["near_label"]
+
+        ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
+        vol_points = vol_points[ind]
+        vol_label = vol_label[ind]
+        vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
+
+        ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
+        near_points = near_points[ind]
+        near_label = near_label[ind]
+        near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
+
+        # concat sampled volume and near points
+        geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
+
+        sample = {
+            "surface": surface,
+            "geo_points": geo_points
+        }
+
+        return sample
+
+
+class SplitRandomSample(object):
+    def __init__(self,
+                 use_surface_sample: bool = False,
+                 num_surface_samples: int = 4096,
+                 num_volume_samples: int = 1024,
+                 num_near_samples: int = 1024):
+
+        super().__init__()
+
+        self.use_surface_sample = use_surface_sample
+        self.num_surface_samples = num_surface_samples
+        self.num_volume_samples = num_volume_samples
+        self.num_near_samples = num_near_samples
+
+    def __call__(self, sample):
+
+        rng = np.random.default_rng()
+
+        # 1. sample surface input
+        surface = sample["surface"]
+
+        if self.use_surface_sample:
+            replace = surface.shape[0] < self.num_surface_samples
+            ind = rng.choice(surface.shape[0], self.num_surface_samples, replace=replace)
+            surface = surface[ind]
+
+        # 2. sample volume/near geometric points
+        vol_points = sample["vol_points"]
+        vol_label = sample["vol_label"]
+        near_points = sample["near_points"]
+        near_label = sample["near_label"]
+
+        ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
+        vol_points = vol_points[ind]
+        vol_label = vol_label[ind]
+        vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
+
+        ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
+        near_points = near_points[ind]
+        near_label = near_label[ind]
+        near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
+
+        # concat sampled volume and near points
+        geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
+
+        sample = {
+            "surface": surface,
+            "geo_points": geo_points
+        }
+
+        return sample
+
+
+class FeatureSelection(object):
+
+    VALID_SURFACE_FEATURE_DIMS = {
+        "none": [0, 1, 2],                              # xyz
+        "watertight_normal": [0, 1, 2, 3, 4, 5],        # xyz, normal
+        "normal": [0, 1, 2, 6, 7, 8]
+    }
+
+    def __init__(self, surface_feature_type: str):
+
+        self.surface_feature_type = surface_feature_type
+        self.surface_dims = self.VALID_SURFACE_FEATURE_DIMS[surface_feature_type]
+
+    def __call__(self, sample):
+        sample["surface"] = sample["surface"][:, self.surface_dims]
+        return sample
+
+
+class AxisScaleTransform(object):
+    def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
+        assert isinstance(interval, (tuple, list, ListConfig))
+        self.interval = interval
+        self.min_val = interval[0]
+        self.max_val = interval[1]
+        self.inter_size = interval[1] - interval[0]
+        self.jitter = jitter
+        self.jitter_scale = jitter_scale
+
+    def __call__(self, sample):
+
+        surface = sample["surface"][..., 0:3]
+        geo_points = sample["geo_points"][..., 0:3]
+
+        scaling = torch.rand(1, 3) * self.inter_size + self.min_val
+        # print(scaling)
+        surface = surface * scaling
+        geo_points = geo_points * scaling
+
+        scale = (1 / torch.abs(surface).max().item()) * 0.999999
+        surface *= scale
+        geo_points *= scale
+
+        if self.jitter:
+            surface += self.jitter_scale * torch.randn_like(surface)
+            surface.clamp_(min=-1.015, max=1.015)
+
+        sample["surface"][..., 0:3] = surface
+        sample["geo_points"][..., 0:3] = geo_points
+
+        return sample
+
+
+class ToTensor(object):
+
+    def __init__(self, tensor_keys=("surface", "geo_points", "tex_points")):
+        self.tensor_keys = tensor_keys
+
+    def __call__(self, sample):
+        for key in self.tensor_keys:
+            if key not in sample:
+                continue
+
+            sample[key] = torch.tensor(sample[key], dtype=torch.float32)
+
+        return sample
+
+
+class AxisScale(object):
+    def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
+        assert isinstance(interval, (tuple, list, ListConfig))
+        self.interval = interval
+        self.jitter = jitter
+        self.jitter_scale = jitter_scale
+
+    def __call__(self, surface, *args):
+        scaling = torch.rand(1, 3) * 0.5 + 0.75
+        # print(scaling)
+        surface = surface * scaling
+        scale = (1 / torch.abs(surface).max().item()) * 0.999999
+        surface *= scale
+
+        args_outputs = []
+        for _arg in args:
+            _arg = _arg * scaling * scale
+            args_outputs.append(_arg)
+
+        if self.jitter:
+            surface += self.jitter_scale * torch.randn_like(surface)
+            surface.clamp_(min=-1, max=1)
+
+        if len(args) == 0:
+            return surface
+        else:
+            return surface, *args_outputs
+
+
+class RandomResize(torch.nn.Module):
+    """Apply randomly Resize with a given probability."""
+
+    def __init__(
+        self,
+        size,
+        resize_radio=(0.5, 1),
+        allow_resize_interpolations=(InterpolationMode.BICUBIC, InterpolationMode.BILINEAR, InterpolationMode.BILINEAR),
+        interpolation=InterpolationMode.BICUBIC,
+        max_size=None,
+        antialias=None,
+    ):
+        super().__init__()
+        if not isinstance(size, (int, Sequence)):
+            raise TypeError(f"Size should be int or sequence. Got {type(size)}")
+        if isinstance(size, Sequence) and len(size) not in (1, 2):
+            raise ValueError("If size is a sequence, it should have 1 or 2 values")
+
+        self.size = size
+        self.max_size = max_size
+        # Backward compatibility with integer value
+        if isinstance(interpolation, int):
+            warnings.warn(
+                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
+                "Please use InterpolationMode enum."
+            )
+            interpolation = _interpolation_modes_from_int(interpolation)
+
+        self.interpolation = interpolation
+        self.antialias = antialias
+
+        self.resize_radio = resize_radio
+        self.allow_resize_interpolations = allow_resize_interpolations
+
+    def random_resize_params(self):
+        radio = torch.rand(1) * (self.resize_radio[1] - self.resize_radio[0]) + self.resize_radio[0]
+
+        if isinstance(self.size, int):
+            size = int(self.size * radio)
+        elif isinstance(self.size, Sequence):
+            size = list(self.size)
+            size = (int(size[0] * radio), int(size[1] * radio))
+        else:
+            raise RuntimeError()
+
+        interpolation = self.allow_resize_interpolations[
+            torch.randint(low=0, high=len(self.allow_resize_interpolations), size=(1,))
+        ]
+        return size, interpolation
+
+    def forward(self, img):
+        size, interpolation = self.random_resize_params()
+        img = TVF.resize(img, size, interpolation, self.max_size, self.antialias)
+        img = TVF.resize(img, self.size, self.interpolation, self.max_size, self.antialias)
+        return img
+
+    def __repr__(self) -> str:
+        detail = f"(size={self.size}, interpolation={self.interpolation.value},"
+        detail += f"max_size={self.max_size}, antialias={self.antialias}), resize_radio={self.resize_radio}"
+        return f"{self.__class__.__name__}{detail}"
+
+
+class Compose(object):
+    """Composes several transforms together. This transform does not support torchscript.
+    Please, see the note below.
+
+    Args:
+        transforms (list of ``Transform`` objects): list of transforms to compose.
+
+    Example:
+        >>> transforms.Compose([
+        >>>     transforms.CenterCrop(10),
+        >>>     transforms.ToTensor(),
+        >>> ])
+
+    .. note::
+        In order to script the transformations, please use ``torch.nn.Sequential`` as below.
+
+        >>> transforms = torch.nn.Sequential(
+        >>>     transforms.CenterCrop(10),
+        >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        >>> )
+        >>> scripted_transforms = torch.jit.script(transforms)
+
+        Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
+        `lambda` functions or ``PIL.Image``.
+
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, *args):
+        for t in self.transforms:
+            args = t(*args)
+        return args
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string
+
+
+def identity(*args, **kwargs):
+    if len(args) == 1:
+        return args[0]
+    else:
+        return args
+
+
+def build_transforms(cfg):
+
+    if cfg is None:
+        return identity
+
+    transforms = []
+
+    for transform_name, cfg_instance in cfg.items():
+        transform_instance = instantiate_from_config(cfg_instance)
+        transforms.append(transform_instance)
+        print(f"Build transform: {transform_instance}")
+
+    transforms = Compose(transforms)
+
+    return transforms
+

third_party/Michelangelo/michelangelo/data/utils.py

@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import numpy as np
+
+
+def worker_init_fn(_):
+    worker_info = torch.utils.data.get_worker_info()
+    worker_id = worker_info.id
+
+    # dataset = worker_info.dataset
+    # split_size = dataset.num_records // worker_info.num_workers
+    # # reset num_records to the true number to retain reliable length information
+    # dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
+    # current_id = np.random.choice(len(np.random.get_state()[1]), 1)
+    # return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
+
+    return np.random.seed(np.random.get_state()[1][0] + worker_id)
+
+
+def collation_fn(samples, combine_tensors=True, combine_scalars=True):
+    """
+
+    Args:
+        samples (list[dict]):
+        combine_tensors:
+        combine_scalars:
+
+    Returns:
+
+    """
+
+    result = {}
+
+    keys = samples[0].keys()
+
+    for key in keys:
+        result[key] = []
+
+    for sample in samples:
+        for key in keys:
+            val = sample[key]
+            result[key].append(val)
+
+    for key in keys:
+        val_list = result[key]
+        if isinstance(val_list[0], (int, float)):
+            if combine_scalars:
+                result[key] = np.array(result[key])
+
+        elif isinstance(val_list[0], torch.Tensor):
+            if combine_tensors:
+                result[key] = torch.stack(val_list)
+
+        elif isinstance(val_list[0], np.ndarray):
+            if combine_tensors:
+                result[key] = np.stack(val_list)
+
+    return result

third_party/Michelangelo/michelangelo/graphics/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

third_party/Michelangelo/michelangelo/graphics/primitives/__init__.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+
+from .volume import generate_dense_grid_points
+
+from .mesh import (
+    MeshOutput,
+    save_obj,
+    savemeshtes2
+)

third_party/Michelangelo/michelangelo/graphics/primitives/mesh.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+
+import os
+import cv2
+import numpy as np
+import PIL.Image
+from typing import Optional
+
+import trimesh
+
+
+def save_obj(pointnp_px3, facenp_fx3, fname):
+    fid = open(fname, "w")
+    write_str = ""
+    for pidx, p in enumerate(pointnp_px3):
+        pp = p
+        write_str += "v %f %f %f\n" % (pp[0], pp[1], pp[2])
+
+    for i, f in enumerate(facenp_fx3):
+        f1 = f + 1
+        write_str += "f %d %d %d\n" % (f1[0], f1[1], f1[2])
+    fid.write(write_str)
+    fid.close()
+    return
+
+
+def savemeshtes2(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, tex_map, fname):
+    fol, na = os.path.split(fname)
+    na, _ = os.path.splitext(na)
+
+    matname = "%s/%s.mtl" % (fol, na)
+    fid = open(matname, "w")
+    fid.write("newmtl material_0\n")
+    fid.write("Kd 1 1 1\n")
+    fid.write("Ka 0 0 0\n")
+    fid.write("Ks 0.4 0.4 0.4\n")
+    fid.write("Ns 10\n")
+    fid.write("illum 2\n")
+    fid.write("map_Kd %s.png\n" % na)
+    fid.close()
+    ####
+
+    fid = open(fname, "w")
+    fid.write("mtllib %s.mtl\n" % na)
+
+    for pidx, p in enumerate(pointnp_px3):
+        pp = p
+        fid.write("v %f %f %f\n" % (pp[0], pp[1], pp[2]))
+
+    for pidx, p in enumerate(tcoords_px2):
+        pp = p
+        fid.write("vt %f %f\n" % (pp[0], pp[1]))
+
+    fid.write("usemtl material_0\n")
+    for i, f in enumerate(facenp_fx3):
+        f1 = f + 1
+        f2 = facetex_fx3[i] + 1
+        fid.write("f %d/%d %d/%d %d/%d\n" % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
+    fid.close()
+
+    PIL.Image.fromarray(np.ascontiguousarray(tex_map), "RGB").save(
+        os.path.join(fol, "%s.png" % na))
+
+    return
+
+
+class MeshOutput(object):
+
+    def __init__(self,
+                 mesh_v: np.ndarray,
+                 mesh_f: np.ndarray,
+                 vertex_colors: Optional[np.ndarray] = None,
+                 uvs: Optional[np.ndarray] = None,
+                 mesh_tex_idx: Optional[np.ndarray] = None,
+                 tex_map: Optional[np.ndarray] = None):
+
+        self.mesh_v = mesh_v
+        self.mesh_f = mesh_f
+        self.vertex_colors = vertex_colors
+        self.uvs = uvs
+        self.mesh_tex_idx = mesh_tex_idx
+        self.tex_map = tex_map
+
+    def contain_uv_texture(self):
+        return (self.uvs is not None) and (self.mesh_tex_idx is not None) and (self.tex_map is not None)
+
+    def contain_vertex_colors(self):
+        return self.vertex_colors is not None
+
+    def export(self, fname):
+
+        if self.contain_uv_texture():
+            savemeshtes2(
+                self.mesh_v,
+                self.uvs,
+                self.mesh_f,
+                self.mesh_tex_idx,
+                self.tex_map,
+                fname
+            )
+
+        elif self.contain_vertex_colors():
+            mesh_obj = trimesh.Trimesh(vertices=self.mesh_v, faces=self.mesh_f, vertex_colors=self.vertex_colors)
+            mesh_obj.export(fname)
+
+        else:
+            save_obj(
+                self.mesh_v,
+                self.mesh_f,
+                fname
+            )
+
+
+

third_party/Michelangelo/michelangelo/graphics/primitives/volume.py

@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+
+def generate_dense_grid_points(bbox_min: np.ndarray,
+                               bbox_max: np.ndarray,
+                               octree_depth: int,
+                               indexing: str = "ij"):
+    length = bbox_max - bbox_min
+    num_cells = np.exp2(octree_depth)
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1)
+    xyz = xyz.reshape(-1, 3)
+    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
+
+    return xyz, grid_size, length
+

third_party/Michelangelo/michelangelo/models/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

third_party/Michelangelo/michelangelo/models/asl_diffusion/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

third_party/Michelangelo/michelangelo/models/asl_diffusion/asl_diffuser_pl_module.py

@@ -0,0 +1,482 @@
+# -*- coding: utf-8 -*-
+
+from omegaconf import DictConfig
+from typing import List, Tuple, Dict, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.optim import lr_scheduler
+import pytorch_lightning as pl
+from pytorch_lightning.utilities import rank_zero_only
+
+from einops import rearrange
+
+from diffusers.schedulers import (
+    DDPMScheduler,
+    DDIMScheduler,
+    KarrasVeScheduler,
+    DPMSolverMultistepScheduler
+)
+
+from third_party.Michelangelo.michelangelo.utils import instantiate_from_config
+from third_party.Michelangelo.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentPLModule
+from third_party.Michelangelo.michelangelo.models.asl_diffusion.inference_utils import ddim_sample
+
+SchedulerType = Union[DDIMScheduler, KarrasVeScheduler, DPMSolverMultistepScheduler]
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class ASLDiffuser(pl.LightningModule):
+    first_stage_model: Optional[AlignedShapeAsLatentPLModule]
+    # cond_stage_model: Optional[Union[nn.Module, pl.LightningModule]]
+    model: nn.Module
+
+    def __init__(self, *,
+                 first_stage_config,
+                 denoiser_cfg,
+                 scheduler_cfg,
+                 optimizer_cfg,
+                 loss_cfg,
+                 first_stage_key: str = "surface",
+                 cond_stage_key: str = "image",
+                 cond_stage_trainable: bool = True,
+                 scale_by_std: bool = False,
+                 z_scale_factor: float = 1.0,
+                 ckpt_path: Optional[str] = None,
+                 ignore_keys: Union[Tuple[str], List[str]] = ()):
+
+        super().__init__()
+
+        self.first_stage_key = first_stage_key
+        self.cond_stage_key = cond_stage_key
+        self.cond_stage_trainable = cond_stage_trainable
+
+        # 1. initialize first stage. 
+        # Note: the condition model contained in the first stage model.
+        self.first_stage_config = first_stage_config
+        self.first_stage_model = None
+        # self.instantiate_first_stage(first_stage_config)
+
+        # 2. initialize conditional stage
+        # self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_model = {
+            "image": self.encode_image,
+            "image_unconditional_embedding": self.empty_img_cond,
+            "text": self.encode_text,
+            "text_unconditional_embedding": self.empty_text_cond,
+            "surface": self.encode_surface,
+            "surface_unconditional_embedding": self.empty_surface_cond,
+        }
+
+        # 3. diffusion model
+        self.model = instantiate_from_config(
+            denoiser_cfg, device=None, dtype=None
+        )
+
+        self.optimizer_cfg = optimizer_cfg
+
+        # 4. scheduling strategy
+        self.scheduler_cfg = scheduler_cfg
+
+        self.noise_scheduler: DDPMScheduler = instantiate_from_config(scheduler_cfg.noise)
+        self.denoise_scheduler: SchedulerType = instantiate_from_config(scheduler_cfg.denoise)
+
+        # 5. loss configures
+        self.loss_cfg = loss_cfg
+
+        self.scale_by_std = scale_by_std
+        if scale_by_std:
+            self.register_buffer("z_scale_factor", torch.tensor(z_scale_factor))
+        else:
+            self.z_scale_factor = z_scale_factor
+
+        self.ckpt_path = ckpt_path
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+        self.first_stage_model = self.first_stage_model.to(self.device)
+
+    # def instantiate_cond_stage(self, config):
+    #     if not self.cond_stage_trainable:
+    #         if config == "__is_first_stage__":
+    #             print("Using first stage also as cond stage.")
+    #             self.cond_stage_model = self.first_stage_model
+    #         elif config == "__is_unconditional__":
+    #             print(f"Training {self.__class__.__name__} as an unconditional model.")
+    #             self.cond_stage_model = None
+    #             # self.be_unconditional = True
+    #         else:
+    #             model = instantiate_from_config(config)
+    #             self.cond_stage_model = model.eval()
+    #             self.cond_stage_model.train = disabled_train
+    #             for param in self.cond_stage_model.parameters():
+    #                 param.requires_grad = False
+    #     else:
+    #         assert config != "__is_first_stage__"
+    #         assert config != "__is_unconditional__"
+    #         model = instantiate_from_config(config)
+    #         self.cond_stage_model = model
+
+    def init_from_ckpt(self, path, ignore_keys=()):
+        state_dict = torch.load(path, map_location="cpu")["state_dict"]
+
+        keys = list(state_dict.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del state_dict[k]
+
+        missing, unexpected = self.load_state_dict(state_dict, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    @property
+    def zero_rank(self):
+        if self._trainer:
+            zero_rank = self.trainer.local_rank == 0
+        else:
+            zero_rank = True
+
+        return zero_rank
+
+    def configure_optimizers(self) -> Tuple[List, List]:
+
+        lr = self.learning_rate
+
+        trainable_parameters = list(self.model.parameters())
+        # if the conditional encoder is trainable
+
+        # if self.cond_stage_trainable:
+        #     conditioner_params = [p for p in self.cond_stage_model.parameters() if p.requires_grad]
+        #     trainable_parameters += conditioner_params
+        #     print(f"number of trainable conditional parameters: {len(conditioner_params)}.")
+
+        if self.optimizer_cfg is None:
+            optimizers = [torch.optim.AdamW(trainable_parameters, lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
+            schedulers = []
+        else:
+            optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=trainable_parameters)
+            scheduler_func = instantiate_from_config(
+                self.optimizer_cfg.scheduler,
+                max_decay_steps=self.trainer.max_steps,
+                lr_max=lr
+            )
+            scheduler = {
+                "scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
+                "interval": "step",
+                "frequency": 1
+            }
+            optimizers = [optimizer]
+            schedulers = [scheduler]
+
+        return optimizers, schedulers
+
+    @torch.no_grad()
+    def encode_text(self, text):
+
+        b = text.shape[0]
+        text_tokens = rearrange(text, "b t l -> (b t) l")
+        text_embed = self.first_stage_model.model.encode_text_embed(text_tokens)
+        text_embed = rearrange(text_embed, "(b t) d -> b t d", b=b)
+        text_embed = text_embed.mean(dim=1)
+        text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
+
+        return text_embed
+
+    @torch.no_grad()
+    def encode_image(self, img):
+
+        return self.first_stage_model.model.encode_image_embed(img)
+
+    @torch.no_grad()
+    def encode_surface(self, surface):
+
+        return self.first_stage_model.model.encode_shape_embed(surface, return_latents=False)
+
+    @torch.no_grad()
+    def empty_text_cond(self, cond):
+
+        return torch.zeros_like(cond, device=cond.device)
+
+    @torch.no_grad()
+    def empty_img_cond(self, cond):
+
+        return torch.zeros_like(cond, device=cond.device)
+
+    @torch.no_grad()
+    def empty_surface_cond(self, cond):
+
+        return torch.zeros_like(cond, device=cond.device)
+
+    @torch.no_grad()
+    def encode_first_stage(self, surface: torch.FloatTensor, sample_posterior=True):
+
+        z_q = self.first_stage_model.encode(surface, sample_posterior)
+        z_q = self.z_scale_factor * z_q
+
+        return z_q
+
+    @torch.no_grad()
+    def decode_first_stage(self, z_q: torch.FloatTensor, **kwargs):
+
+        z_q = 1. / self.z_scale_factor * z_q
+        latents = self.first_stage_model.decode(z_q, **kwargs)
+        return latents
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 \
+                and batch_idx == 0 and self.ckpt_path is None:
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+
+            z_q = self.encode_first_stage(batch[self.first_stage_key])
+            z = z_q.detach()
+
+            del self.z_scale_factor
+            self.register_buffer("z_scale_factor", 1. / z.flatten().std())
+            print(f"setting self.z_scale_factor to {self.z_scale_factor}")
+
+            print("### USING STD-RESCALING ###")
+
+    def compute_loss(self, model_outputs, split):
+        """
+
+        Args:
+            model_outputs (dict):
+                - x_0:
+                - noise:
+                - noise_prior:
+                - noise_pred:
+                - noise_pred_prior:
+
+            split (str):
+
+        Returns:
+
+        """
+
+        pred = model_outputs["pred"]
+
+        if self.noise_scheduler.prediction_type == "epsilon":
+            target = model_outputs["noise"]
+        elif self.noise_scheduler.prediction_type == "sample":
+            target = model_outputs["x_0"]
+        else:
+            raise NotImplementedError(f"Prediction Type: {self.noise_scheduler.prediction_type} not yet supported.")
+
+        if self.loss_cfg.loss_type == "l1":
+            simple = F.l1_loss(pred, target, reduction="mean")
+        elif self.loss_cfg.loss_type in ["mse", "l2"]:
+            simple = F.mse_loss(pred, target, reduction="mean")
+        else:
+            raise NotImplementedError(f"Loss Type: {self.loss_cfg.loss_type} not yet supported.")
+
+        total_loss = simple
+
+        loss_dict = {
+            f"{split}/total_loss": total_loss.clone().detach(),
+            f"{split}/simple": simple.detach(),
+        }
+
+        return total_loss, loss_dict
+
+    def forward(self, batch):
+        """
+
+        Args:
+            batch:
+
+        Returns:
+
+        """
+
+        if self.first_stage_model is None:
+            self.instantiate_first_stage(self.first_stage_config)
+
+        latents = self.encode_first_stage(batch[self.first_stage_key])
+
+        # conditions = self.cond_stage_model.encode(batch[self.cond_stage_key])
+
+        conditions = self.cond_stage_model[self.cond_stage_key](batch[self.cond_stage_key]).unsqueeze(1)
+
+        mask = torch.rand((len(conditions), 1, 1), device=conditions.device, dtype=conditions.dtype) >= 0.1
+        conditions = conditions * mask.to(conditions)
+
+        # Sample noise that we"ll add to the latents
+        # [batch_size, n_token, latent_dim]
+        noise = torch.randn_like(latents)
+        bs = latents.shape[0]
+        # Sample a random timestep for each motion
+        timesteps = torch.randint(
+            0,
+            self.noise_scheduler.config.num_train_timesteps,
+            (bs,),
+            device=latents.device,
+        )
+        timesteps = timesteps.long()
+        # Add noise to the latents according to the noise magnitude at each timestep
+        noisy_z = self.noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # diffusion model forward
+        noise_pred = self.model(noisy_z, timesteps, conditions)
+
+        diffusion_outputs = {
+            "x_0": noisy_z,
+            "noise": noise,
+            "pred": noise_pred
+        }
+
+        return diffusion_outputs
+
+    def training_step(self, batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+                      batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
+        """
+
+        Args:
+            batch (dict): the batch sample, and it contains:
+                - surface (torch.FloatTensor):
+                - image (torch.FloatTensor): if provide, [bs, 3, h, w], item range [0, 1]
+                - depth (torch.FloatTensor): if provide, [bs, 1, h, w], item range [-1, 1]
+                - normal (torch.FloatTensor): if provide, [bs, 3, h, w], item range [-1, 1]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "train")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    def validation_step(self, batch: Dict[str, torch.FloatTensor],
+                        batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
+        """
+
+        Args:
+            batch (dict): the batch sample, and it contains:
+                - surface_pc (torch.FloatTensor): [n_pts, 4]
+                - surface_feats (torch.FloatTensor): [n_pts, c]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "val")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    @torch.no_grad()
+    def sample(self,
+               batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+               sample_times: int = 1,
+               steps: Optional[int] = None,
+               guidance_scale: Optional[float] = None,
+               eta: float = 0.0,
+               return_intermediates: bool = False, **kwargs):
+
+        if self.first_stage_model is None:
+            self.instantiate_first_stage(self.first_stage_config)
+
+        if steps is None:
+            steps = self.scheduler_cfg.num_inference_steps
+
+        if guidance_scale is None:
+            guidance_scale = self.scheduler_cfg.guidance_scale
+        do_classifier_free_guidance = guidance_scale > 0
+
+        # conditional encode
+        xc = batch[self.cond_stage_key]
+        # cond = self.cond_stage_model[self.cond_stage_key](xc)
+        cond = self.cond_stage_model[self.cond_stage_key](xc).unsqueeze(1)
+
+        if do_classifier_free_guidance:
+            """
+            Note: There are two kinds of uncond for text. 
+            1: using "" as uncond text; (in SAL diffusion)
+            2: zeros_like(cond) as uncond text; (in MDM)
+            """
+            # un_cond = self.cond_stage_model.unconditional_embedding(batch_size=len(xc))
+            un_cond = self.cond_stage_model[f"{self.cond_stage_key}_unconditional_embedding"](cond)
+            # un_cond = torch.zeros_like(cond, device=cond.device)
+            cond = torch.cat([un_cond, cond], dim=0)
+
+        outputs = []
+        latents = None
+
+        if not return_intermediates:
+            for _ in range(sample_times):
+                sample_loop = ddim_sample(
+                    self.denoise_scheduler,
+                    self.model,
+                    shape=self.first_stage_model.latent_shape,
+                    cond=cond,
+                    steps=steps,
+                    guidance_scale=guidance_scale,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    device=self.device,
+                    eta=eta,
+                    disable_prog=not self.zero_rank
+                )
+                for sample, t in sample_loop:
+                    latents = sample
+                outputs.append(self.decode_first_stage(latents, **kwargs))
+        else:
+
+            sample_loop = ddim_sample(
+                self.denoise_scheduler,
+                self.model,
+                shape=self.first_stage_model.latent_shape,
+                cond=cond,
+                steps=steps,
+                guidance_scale=guidance_scale,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                device=self.device,
+                eta=eta,
+                disable_prog=not self.zero_rank
+            )
+
+            iter_size = steps // sample_times
+            i = 0
+            for sample, t in sample_loop:
+                latents = sample
+                if i % iter_size == 0 or i == steps - 1:
+                    outputs.append(self.decode_first_stage(latents, **kwargs))
+                i += 1
+
+        return outputs

third_party/Michelangelo/michelangelo/models/asl_diffusion/asl_udt.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+from typing import Optional
+from diffusers.models.embeddings import Timesteps
+import math
+
+from third_party.Michelangelo.michelangelo.models.modules.transformer_blocks import MLP
+from third_party.Michelangelo.michelangelo.models.modules.diffusion_transformer import UNetDiffusionTransformer
+
+
+class ConditionalASLUDTDenoiser(nn.Module):
+
+    def __init__(self, *,
+                 device: Optional[torch.device],
+                 dtype: Optional[torch.dtype],
+                 input_channels: int,
+                 output_channels: int,
+                 n_ctx: int,
+                 width: int,
+                 layers: int,
+                 heads: int,
+                 context_dim: int,
+                 context_ln: bool = True,
+                 skip_ln: bool = False,
+                 init_scale: float = 0.25,
+                 flip_sin_to_cos: bool = False,
+                 use_checkpoint: bool = False):
+        super().__init__()
+
+        self.use_checkpoint = use_checkpoint
+
+        init_scale = init_scale * math.sqrt(1.0 / width)
+
+        self.backbone = UNetDiffusionTransformer(
+            device=device,
+            dtype=dtype,
+            n_ctx=n_ctx,
+            width=width,
+            layers=layers,
+            heads=heads,
+            skip_ln=skip_ln,
+            init_scale=init_scale,
+            use_checkpoint=use_checkpoint
+        )
+        self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype)
+        self.input_proj = nn.Linear(input_channels, width, device=device, dtype=dtype)
+        self.output_proj = nn.Linear(width, output_channels, device=device, dtype=dtype)
+
+        # timestep embedding
+        self.time_embed = Timesteps(width, flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=0)
+        self.time_proj = MLP(
+            device=device, dtype=dtype, width=width, init_scale=init_scale
+        )
+
+        self.context_embed = nn.Sequential(
+            nn.LayerNorm(context_dim, device=device, dtype=dtype),
+            nn.Linear(context_dim, width, device=device, dtype=dtype),
+        )
+
+        if context_ln:
+            self.context_embed = nn.Sequential(
+                nn.LayerNorm(context_dim, device=device, dtype=dtype),
+                nn.Linear(context_dim, width, device=device, dtype=dtype),
+            )
+        else:
+            self.context_embed = nn.Linear(context_dim, width, device=device, dtype=dtype)
+
+    def forward(self,
+                model_input: torch.FloatTensor,
+                timestep: torch.LongTensor,
+                context: torch.FloatTensor):
+
+        r"""
+        Args:
+            model_input (torch.FloatTensor): [bs, n_data, c]
+            timestep (torch.LongTensor): [bs,]
+            context (torch.FloatTensor): [bs, context_tokens, c]
+
+        Returns:
+            sample (torch.FloatTensor): [bs, n_data, c]
+
+        """
+
+        _, n_data, _ = model_input.shape
+
+        # 1. time
+        t_emb = self.time_proj(self.time_embed(timestep)).unsqueeze(dim=1)
+
+        # 2. conditions projector
+        context = self.context_embed(context)
+
+        # 3. denoiser
+        x = self.input_proj(model_input)
+        x = torch.cat([t_emb, context, x], dim=1)
+        x = self.backbone(x)
+        x = self.ln_post(x)
+        x = x[:, -n_data:]
+        sample = self.output_proj(x)
+
+        return sample
+
+

third_party/Michelangelo/michelangelo/models/asl_diffusion/base.py

@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+
+
+class BaseDenoiser(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, t, context):
+        raise NotImplementedError

third_party/Michelangelo/michelangelo/models/asl_diffusion/clip_asl_diffuser_pl_module.py

@@ -0,0 +1,393 @@
+# -*- coding: utf-8 -*-
+
+from omegaconf import DictConfig
+from typing import List, Tuple, Dict, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.optim import lr_scheduler
+import pytorch_lightning as pl
+from pytorch_lightning.utilities import rank_zero_only
+
+from diffusers.schedulers import (
+    DDPMScheduler,
+    DDIMScheduler,
+    KarrasVeScheduler,
+    DPMSolverMultistepScheduler
+)
+
+from third_party.Michelangelo.michelangelo.utils import instantiate_from_config
+from third_party.Michelangelo.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentPLModule
+from third_party.Michelangelo.michelangelo.models.asl_diffusion.inference_utils import ddim_sample
+
+SchedulerType = Union[DDIMScheduler, KarrasVeScheduler, DPMSolverMultistepScheduler]
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class ClipASLDiffuser(pl.LightningModule):
+    first_stage_model: Optional[AlignedShapeAsLatentPLModule]
+    cond_stage_model: Optional[Union[nn.Module, pl.LightningModule]]
+    model: nn.Module
+
+    def __init__(self, *,
+                 first_stage_config,
+                 cond_stage_config,
+                 denoiser_cfg,
+                 scheduler_cfg,
+                 optimizer_cfg,
+                 loss_cfg,
+                 first_stage_key: str = "surface",
+                 cond_stage_key: str = "image",
+                 scale_by_std: bool = False,
+                 z_scale_factor: float = 1.0,
+                 ckpt_path: Optional[str] = None,
+                 ignore_keys: Union[Tuple[str], List[str]] = ()):
+
+        super().__init__()
+
+        self.first_stage_key = first_stage_key
+        self.cond_stage_key = cond_stage_key
+
+        # 1. lazy initialize first stage
+        self.instantiate_first_stage(first_stage_config)
+
+        # 2. initialize conditional stage
+        self.instantiate_cond_stage(cond_stage_config)
+
+        # 3. diffusion model
+        self.model = instantiate_from_config(
+            denoiser_cfg, device=None, dtype=None
+        )
+
+        self.optimizer_cfg = optimizer_cfg
+
+        # 4. scheduling strategy
+        self.scheduler_cfg = scheduler_cfg
+
+        self.noise_scheduler: DDPMScheduler = instantiate_from_config(scheduler_cfg.noise)
+        self.denoise_scheduler: SchedulerType = instantiate_from_config(scheduler_cfg.denoise)
+
+        # 5. loss configures
+        self.loss_cfg = loss_cfg
+
+        self.scale_by_std = scale_by_std
+        if scale_by_std:
+            self.register_buffer("z_scale_factor", torch.tensor(z_scale_factor))
+        else:
+            self.z_scale_factor = z_scale_factor
+
+        self.ckpt_path = ckpt_path
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def instantiate_non_trainable_model(self, config):
+        model = instantiate_from_config(config)
+        model = model.eval()
+        model.train = disabled_train
+        for param in model.parameters():
+            param.requires_grad = False
+
+        return model
+
+    def instantiate_first_stage(self, first_stage_config):
+        self.first_stage_model = self.instantiate_non_trainable_model(first_stage_config)
+        self.first_stage_model.set_shape_model_only()
+
+    def instantiate_cond_stage(self, cond_stage_config):
+        self.cond_stage_model = self.instantiate_non_trainable_model(cond_stage_config)
+
+    def init_from_ckpt(self, path, ignore_keys=()):
+        state_dict = torch.load(path, map_location="cpu")["state_dict"]
+
+        keys = list(state_dict.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del state_dict[k]
+
+        missing, unexpected = self.load_state_dict(state_dict, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    @property
+    def zero_rank(self):
+        if self._trainer:
+            zero_rank = self.trainer.local_rank == 0
+        else:
+            zero_rank = True
+
+        return zero_rank
+
+    def configure_optimizers(self) -> Tuple[List, List]:
+
+        lr = self.learning_rate
+
+        trainable_parameters = list(self.model.parameters())
+        if self.optimizer_cfg is None:
+            optimizers = [torch.optim.AdamW(trainable_parameters, lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
+            schedulers = []
+        else:
+            optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=trainable_parameters)
+            scheduler_func = instantiate_from_config(
+                self.optimizer_cfg.scheduler,
+                max_decay_steps=self.trainer.max_steps,
+                lr_max=lr
+            )
+            scheduler = {
+                "scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
+                "interval": "step",
+                "frequency": 1
+            }
+            optimizers = [optimizer]
+            schedulers = [scheduler]
+
+        return optimizers, schedulers
+
+    @torch.no_grad()
+    def encode_first_stage(self, surface: torch.FloatTensor, sample_posterior=True):
+
+        z_q = self.first_stage_model.encode(surface, sample_posterior)
+        z_q = self.z_scale_factor * z_q
+
+        return z_q
+
+    @torch.no_grad()
+    def decode_first_stage(self, z_q: torch.FloatTensor, **kwargs):
+
+        z_q = 1. / self.z_scale_factor * z_q
+        latents = self.first_stage_model.decode(z_q, **kwargs)
+        return latents
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 \
+                and batch_idx == 0 and self.ckpt_path is None:
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+
+            z_q = self.encode_first_stage(batch[self.first_stage_key])
+            z = z_q.detach()
+
+            del self.z_scale_factor
+            self.register_buffer("z_scale_factor", 1. / z.flatten().std())
+            print(f"setting self.z_scale_factor to {self.z_scale_factor}")
+
+            print("### USING STD-RESCALING ###")
+
+    def compute_loss(self, model_outputs, split):
+        """
+
+        Args:
+            model_outputs (dict):
+                - x_0:
+                - noise:
+                - noise_prior:
+                - noise_pred:
+                - noise_pred_prior:
+
+            split (str):
+
+        Returns:
+
+        """
+
+        pred = model_outputs["pred"]
+
+        if self.noise_scheduler.prediction_type == "epsilon":
+            target = model_outputs["noise"]
+        elif self.noise_scheduler.prediction_type == "sample":
+            target = model_outputs["x_0"]
+        else:
+            raise NotImplementedError(f"Prediction Type: {self.noise_scheduler.prediction_type} not yet supported.")
+
+        if self.loss_cfg.loss_type == "l1":
+            simple = F.l1_loss(pred, target, reduction="mean")
+        elif self.loss_cfg.loss_type in ["mse", "l2"]:
+            simple = F.mse_loss(pred, target, reduction="mean")
+        else:
+            raise NotImplementedError(f"Loss Type: {self.loss_cfg.loss_type} not yet supported.")
+
+        total_loss = simple
+
+        loss_dict = {
+            f"{split}/total_loss": total_loss.clone().detach(),
+            f"{split}/simple": simple.detach(),
+        }
+
+        return total_loss, loss_dict
+
+    def forward(self, batch):
+        """
+
+        Args:
+            batch:
+
+        Returns:
+
+        """
+
+        latents = self.encode_first_stage(batch[self.first_stage_key])
+        conditions = self.cond_stage_model.encode(batch[self.cond_stage_key])
+
+        # Sample noise that we"ll add to the latents
+        # [batch_size, n_token, latent_dim]
+        noise = torch.randn_like(latents)
+        bs = latents.shape[0]
+        # Sample a random timestep for each motion
+        timesteps = torch.randint(
+            0,
+            self.noise_scheduler.config.num_train_timesteps,
+            (bs,),
+            device=latents.device,
+        )
+        timesteps = timesteps.long()
+        # Add noise to the latents according to the noise magnitude at each timestep
+        noisy_z = self.noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # diffusion model forward
+        noise_pred = self.model(noisy_z, timesteps, conditions)
+
+        diffusion_outputs = {
+            "x_0": noisy_z,
+            "noise": noise,
+            "pred": noise_pred
+        }
+
+        return diffusion_outputs
+
+    def training_step(self, batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+                      batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
+        """
+
+        Args:
+            batch (dict): the batch sample, and it contains:
+                - surface (torch.FloatTensor):
+                - image (torch.FloatTensor): if provide, [bs, 3, h, w], item range [0, 1]
+                - depth (torch.FloatTensor): if provide, [bs, 1, h, w], item range [-1, 1]
+                - normal (torch.FloatTensor): if provide, [bs, 3, h, w], item range [-1, 1]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "train")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    def validation_step(self, batch: Dict[str, torch.FloatTensor],
+                        batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
+        """
+
+        Args:
+            batch (dict): the batch sample, and it contains:
+                - surface_pc (torch.FloatTensor): [n_pts, 4]
+                - surface_feats (torch.FloatTensor): [n_pts, c]
+                - text (list of str):
+
+            batch_idx (int):
+
+            optimizer_idx (int):
+
+        Returns:
+            loss (torch.FloatTensor):
+
+        """
+
+        diffusion_outputs = self(batch)
+
+        loss, loss_dict = self.compute_loss(diffusion_outputs, "val")
+        self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
+
+        return loss
+
+    @torch.no_grad()
+    def sample(self,
+               batch: Dict[str, Union[torch.FloatTensor, List[str]]],
+               sample_times: int = 1,
+               steps: Optional[int] = None,
+               guidance_scale: Optional[float] = None,
+               eta: float = 0.0,
+               return_intermediates: bool = False, **kwargs):
+
+        if steps is None:
+            steps = self.scheduler_cfg.num_inference_steps
+
+        if guidance_scale is None:
+            guidance_scale = self.scheduler_cfg.guidance_scale
+        do_classifier_free_guidance = guidance_scale > 0
+
+        # conditional encode
+        xc = batch[self.cond_stage_key]
+
+        # print(self.first_stage_model.device, self.cond_stage_model.device, self.device)
+
+        cond = self.cond_stage_model(xc)
+
+        if do_classifier_free_guidance:
+            un_cond = self.cond_stage_model.unconditional_embedding(batch_size=len(xc))
+            cond = torch.cat([un_cond, cond], dim=0)
+
+        outputs = []
+        latents = None
+
+        if not return_intermediates:
+            for _ in range(sample_times):
+                sample_loop = ddim_sample(
+                    self.denoise_scheduler,
+                    self.model,
+                    shape=self.first_stage_model.latent_shape,
+                    cond=cond,
+                    steps=steps,
+                    guidance_scale=guidance_scale,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    device=self.device,
+                    eta=eta,
+                    disable_prog=not self.zero_rank
+                )
+                for sample, t in sample_loop:
+                    latents = sample
+                outputs.append(self.decode_first_stage(latents, **kwargs))
+        else:
+
+            sample_loop = ddim_sample(
+                self.denoise_scheduler,
+                self.model,
+                shape=self.first_stage_model.latent_shape,
+                cond=cond,
+                steps=steps,
+                guidance_scale=guidance_scale,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                device=self.device,
+                eta=eta,
+                disable_prog=not self.zero_rank
+            )
+
+            iter_size = steps // sample_times
+            i = 0
+            for sample, t in sample_loop:
+                latents = sample
+                if i % iter_size == 0 or i == steps - 1:
+                    outputs.append(self.decode_first_stage(latents, **kwargs))
+                i += 1
+
+        return outputs

third_party/Michelangelo/michelangelo/models/asl_diffusion/inference_utils.py

@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+
+import torch
+from tqdm import tqdm
+from typing import Tuple, List, Union, Optional
+from diffusers.schedulers import DDIMScheduler
+
+
+__all__ = ["ddim_sample"]
+
+
+def ddim_sample(ddim_scheduler: DDIMScheduler,
+                diffusion_model: torch.nn.Module,
+                shape: Union[List[int], Tuple[int]],
+                cond: torch.FloatTensor,
+                steps: int,
+                eta: float = 0.0,
+                guidance_scale: float = 3.0,
+                do_classifier_free_guidance: bool = True,
+                generator: Optional[torch.Generator] = None,
+                device: torch.device = "cuda:0",
+                disable_prog: bool = True):
+
+    assert steps > 0, f"{steps} must > 0."
+
+    # init latents
+    bsz = cond.shape[0]
+    if do_classifier_free_guidance:
+        bsz = bsz // 2
+
+    latents = torch.randn(
+        (bsz, *shape),
+        generator=generator,
+        device=cond.device,
+        dtype=cond.dtype,
+    )
+    # scale the initial noise by the standard deviation required by the scheduler
+    latents = latents * ddim_scheduler.init_noise_sigma
+    # set timesteps
+    ddim_scheduler.set_timesteps(steps)
+    timesteps = ddim_scheduler.timesteps.to(device)
+    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+    # eta (η) is only used with the DDIMScheduler, and between [0, 1]
+    extra_step_kwargs = {
+        "eta": eta,
+        "generator": generator
+    }
+
+    # reverse
+    for i, t in enumerate(tqdm(timesteps, disable=disable_prog, desc="DDIM Sampling:", leave=False)):
+        # expand the latents if we are doing classifier free guidance
+        latent_model_input = (
+            torch.cat([latents] * 2)
+            if do_classifier_free_guidance
+            else latents
+        )
+        # latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+        # predict the noise residual
+        timestep_tensor = torch.tensor([t], dtype=torch.long, device=device)
+        timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0])
+        noise_pred = diffusion_model.forward(latent_model_input, timestep_tensor, cond)
+
+        # perform guidance
+        if do_classifier_free_guidance:
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+            )
+            # text_embeddings_for_guidance = encoder_hidden_states.chunk(
+            #     2)[1] if do_classifier_free_guidance else encoder_hidden_states
+        # compute the previous noisy sample x_t -> x_t-1
+        latents = ddim_scheduler.step(
+            noise_pred, t, latents, **extra_step_kwargs
+        ).prev_sample
+
+        yield latents, t
+
+
+def karra_sample():
+    pass

third_party/Michelangelo/michelangelo/models/conditional_encoders/__init__.py

@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .clip import CLIPEncoder

third_party/Michelangelo/michelangelo/models/conditional_encoders/clip.py

@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import numpy as np
+from PIL import Image
+from dataclasses import dataclass
+from torchvision.transforms import Normalize
+from transformers import CLIPModel, CLIPTokenizer
+from transformers.utils import ModelOutput
+from typing import Iterable, Optional, Union, List
+
+
+ImageType = Union[np.ndarray, torch.Tensor, Image.Image]
+
+
+@dataclass
+class CLIPEmbedOutput(ModelOutput):
+    last_hidden_state: torch.FloatTensor = None
+    pooler_output: torch.FloatTensor = None
+    embeds: torch.FloatTensor = None
+
+
+class CLIPEncoder(torch.nn.Module):
+
+    def __init__(self, model_path="openai/clip-vit-base-patch32"):
+
+        super().__init__()
+
+        # Load the CLIP model and processor
+        self.model: CLIPModel = CLIPModel.from_pretrained(model_path)
+        self.tokenizer = CLIPTokenizer.from_pretrained(model_path)
+        self.image_preprocess = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+        self.model.training = False
+        for p in self.model.parameters():
+            p.requires_grad = False
+
+    @torch.no_grad()
+    def encode_image(self, images: Iterable[Optional[ImageType]]):
+        pixel_values = self.image_preprocess(images)
+
+        vision_outputs = self.model.vision_model(pixel_values=pixel_values)
+
+        pooler_output = vision_outputs[1]  # pooled_output
+        image_features = self.model.visual_projection(pooler_output)
+
+        visual_embeds = CLIPEmbedOutput(
+            last_hidden_state=vision_outputs.last_hidden_state,
+            pooler_output=pooler_output,
+            embeds=image_features
+        )
+
+        return visual_embeds
+
+    @torch.no_grad()
+    def encode_text(self, texts: List[str]):
+        text_inputs = self.tokenizer(texts, padding=True, return_tensors="pt")
+
+        text_outputs = self.model.text_model(input_ids=text_inputs)
+
+        pooler_output = text_outputs[1]  # pooled_output
+        text_features = self.model.text_projection(pooler_output)
+
+        text_embeds = CLIPEmbedOutput(
+            last_hidden_state=text_outputs.last_hidden_state,
+            pooler_output=pooler_output,
+            embeds=text_features
+        )
+
+        return text_embeds
+
+    def forward(self,
+                images: Iterable[Optional[ImageType]],
+                texts: List[str]):
+
+        visual_embeds = self.encode_image(images)
+        text_embeds = self.encode_text(texts)
+
+        return visual_embeds, text_embeds
+
+
+
+
+
+
+
+
+
+

third_party/Michelangelo/michelangelo/models/conditional_encoders/encoder_factory.py

@@ -0,0 +1,562 @@
+# -*- coding: utf-8 -*-
+import os
+
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from transformers import CLIPModel, CLIPTokenizer
+from collections import OrderedDict
+
+from third_party.Michelangelo.michelangelo.data.transforms import RandomResize
+
+
+class AbstractEncoder(nn.Module):
+    embedding_dim: int
+
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key="class"):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+
+    def forward(self, batch, key=None):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        c = self.embedding(c)
+        return c
+
+
+class FrozenCLIPTextEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
+    def __init__(
+        self,
+        version="openai/clip-vit-large-patch14",
+        tokenizer_version=None,
+        device="cuda",
+        max_length=77,
+        zero_embedding_radio: float = 0.1,
+    ):
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_version or version)
+
+        self.device = device
+        self.max_length = max_length
+        self.zero_embedding_radio = zero_embedding_radio
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        transformer = CLIPModel.from_pretrained(version).text_model
+
+        for param in transformer.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = transformer
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size):
+        empty_text = [""] * batch_size
+        empty_z = self.forward(empty_text)
+        return empty_z
+
+    def forward(self, text):
+        self.move()
+
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.clip(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        batch_size = len(text)
+        batch_mask = torch.rand((batch_size,))
+        for i in range(batch_size):
+            if batch_mask[i] < self.zero_embedding_radio:
+                text[i] = ""
+
+        return self(text)
+
+class FrozenAlignedCLIPTextEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
+    def __init__(
+        self,
+        version="openai/clip-vit-large-patch14",
+        tokenizer_version=None,
+        device="cuda",
+        max_length=77,
+        zero_embedding_radio: float = 0.1,
+    ):
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_version or version)
+
+        self.device = device
+        self.max_length = max_length
+        self.zero_embedding_radio = zero_embedding_radio
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        transformer = CLIPModel.from_pretrained(version).text_model
+
+        for param in transformer.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = transformer
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size):
+        empty_text = [""] * batch_size
+        empty_z = self.forward(empty_text)
+        return empty_z
+
+    def forward(self, text):
+        self.move()
+
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.clip(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        batch_size = len(text)
+        batch_mask = torch.rand((batch_size,))
+        for i in range(batch_size):
+            if batch_mask[i] < self.zero_embedding_radio:
+                text[i] = ""
+
+        return self(text)
+
+
+class FrozenCLIPImageEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+
+    def __init__(
+            self,
+            version="openai/clip-vit-large-patch14",
+            device="cuda",
+            zero_embedding_radio=0.1,
+            normalize_embedding=True,
+            num_projection_vector=0,
+            linear_mapping_bias=True,
+            reverse_visual_projection=False,
+    ):
+        super().__init__()
+
+        self.device = device
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        clip_model = CLIPModel.from_pretrained(version)
+        clip_model.text_model = None
+        clip_model.text_projection = None
+        clip_model = clip_model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = clip_model
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
+                transforms.CenterCrop(224),  # crop a (224, 224) square
+                transforms.Normalize(
+                    mean=[0.48145466, 0.4578275, 0.40821073],
+                    std=[0.26862954, 0.26130258, 0.27577711],
+                ),
+            ]
+        )
+        self.zero_embedding_radio = zero_embedding_radio
+
+        self.num_projection_vector = num_projection_vector
+        self.reverse_visual_projection = reverse_visual_projection
+        self.normalize_embedding = normalize_embedding
+
+        embedding_dim = (
+            clip_model.visual_projection.in_features
+            if reverse_visual_projection
+            else clip_model.visual_projection.out_features
+        )
+        self.embedding_dim = embedding_dim
+        if self.num_projection_vector > 0:
+            self.projection = nn.Linear(
+                embedding_dim,
+                clip_model.visual_projection.out_features * num_projection_vector,
+                bias=linear_mapping_bias,
+            )
+            nn.init.normal_(self.projection.weight, std=embedding_dim ** -0.5)
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def unconditional_embedding(self, batch_size):
+        zero = torch.zeros(
+            batch_size,
+            1,
+            self.embedding_dim,
+            device=self.device,
+            dtype=self.clip.visual_projection.weight.dtype,
+        )
+        if self.num_projection_vector > 0:
+            zero = self.projection(zero).view(batch_size, self.num_projection_vector, -1)
+        return zero
+
+    def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.device, dtype=self.clip.visual_projection.weight.dtype)
+
+        if self.reverse_visual_projection:
+            z = self.clip.vision_model(self.transform(image))[1]
+        else:
+            z = self.clip.get_image_features(self.transform(image))
+
+        if self.normalize_embedding:
+            z = z / z.norm(dim=-1, keepdim=True)
+        if z.ndim == 2:
+            z = z.unsqueeze(dim=-2)
+
+        if zero_embedding_radio > 0:
+            mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) < zero_embedding_radio
+            z = z * mask.to(z)
+
+        if self.num_projection_vector > 0:
+            z = self.projection(z).view(len(image), self.num_projection_vector, -1)
+
+        return z
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def encode(self, image):
+        self.move()
+        return self(image, zero_embedding_radio=self.zero_embedding_radio)
+
+
+class FrozenCLIPImageGridEmbedder(AbstractEncoder):
+
+    def __init__(
+            self,
+            version="openai/clip-vit-large-patch14",
+            device="cuda",
+            zero_embedding_radio=0.1,
+    ):
+        super().__init__()
+
+        self.device = device
+
+        self.clip_dict = OrderedDict()
+        self.clip_name = os.path.split(version)[-1]
+
+        clip_model: CLIPModel = CLIPModel.from_pretrained(version)
+        clip_model.text_model = None
+        clip_model.text_projection = None
+        clip_model = clip_model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+        self.clip_dict[self.clip_name] = clip_model
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(224, transforms.InterpolationMode.BILINEAR, antialias=True),
+                transforms.CenterCrop(224),  # crop a (224, 224) square
+                transforms.Normalize(
+                    mean=[0.48145466, 0.4578275, 0.40821073],
+                    std=[0.26862954, 0.26130258, 0.27577711],
+                ),
+            ]
+        )
+        self.zero_embedding_radio = zero_embedding_radio
+        self.embedding_dim = clip_model.vision_embed_dim
+
+        self._move_flag = False
+
+    @property
+    def clip(self):
+        return self.clip_dict[self.clip_name]
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size):
+        zero = torch.zeros(
+            batch_size,
+            self.clip.vision_model.embeddings.num_positions,
+            self.embedding_dim,
+            device=self.device,
+            dtype=self.clip.visual_projection.weight.dtype,
+        )
+        return zero
+
+    def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
+        self.move()
+
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.device, dtype=self.clip.visual_projection.weight.dtype)
+
+        z = self.clip.vision_model(self.transform(image)).last_hidden_state
+
+        if zero_embedding_radio > 0:
+            mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) >= zero_embedding_radio
+            z = z * mask.to(z)
+
+        return z
+
+    def encode(self, image):
+        return self(image, zero_embedding_radio=self.zero_embedding_radio)
+
+
+class MoECLIPImageEncoder(nn.Module):
+    def __init__(
+            self,
+            versions,
+            hidden_state_dim,
+            num_projection_vector=8,
+            zero_embedding_radio=0.1,
+            device="cuda",
+            precision="fp16",
+            normalize=False,
+            clip_max=0,
+            transform_type="base",
+            argument_p=0.2,
+    ):
+        super().__init__()
+
+        self.device = torch.device(device)
+        self.hidden_state_dim = hidden_state_dim
+        self.zero_embedding_radio = zero_embedding_radio
+        self.num_projection_vector = num_projection_vector
+        self.dtype = dict(fp16=torch.float16, fp32=torch.float32, bf16=torch.bfloat16)[precision]
+        self.normalize = normalize
+        self.clip_max = clip_max
+
+        if transform_type == "base":
+            self.transform = transforms.Compose(
+                [
+                    transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
+                    transforms.CenterCrop(224),  # crop a (224, 224) square
+                    transforms.Normalize(
+                        mean=[0.48145466, 0.4578275, 0.40821073],
+                        std=[0.26862954, 0.26130258, 0.27577711],
+                    ),
+                ]
+            )
+        elif transform_type == "crop_blur_resize":
+            self.transform = transforms.Compose(
+                [
+                    transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
+                    transforms.CenterCrop(224),  # crop a (224, 224) square
+                    transforms.RandomApply(
+                        transforms=[
+                            transforms.RandomResizedCrop(
+                                size=224,
+                                scale=(0.8, 1.0),
+                                ratio=(0.99, 1.01),
+                                interpolation=transforms.InterpolationMode.BICUBIC,
+                            ),
+                        ],
+                        p=argument_p,
+                    ),
+                    transforms.RandomApply(
+                        transforms=[
+                            transforms.GaussianBlur(kernel_size=9, sigma=(0.1, 5)),
+                        ],
+                        p=argument_p,
+                    ),
+                    transforms.RandomApply(
+                        transforms=[
+                            RandomResize(size=224, resize_radio=(0.2, 1)),
+                        ],
+                        p=argument_p,
+                    ),
+                    transforms.Normalize(
+                        mean=[0.48145466, 0.4578275, 0.40821073],
+                        std=[0.26862954, 0.26130258, 0.27577711],
+                    ),
+                ]
+            )
+        else:
+            raise ValueError(f"invalid {transform_type=}")
+
+        if isinstance(versions, str):
+            versions = (versions,)
+
+        # 如果直接把clips定位为当前类的子module，1. 会在保存ckp时存无用的多个权重。 2. pl会调用to，导致layer_norm的权重也被转换成fp16
+        clips = OrderedDict()
+
+        for v in versions:
+            # 因为clips不是子module，直接指定device="cuda"会错误地导致clip模型权重都被放到cuda:0上。
+            clips[v], _ = clip.load(name=v, device="cpu", jit=False, download_root=None)
+            delattr(clips[v], "transformer")
+            clips[v].eval()
+            clips[v].requires_grad_(False)
+
+        self.clips_hidden_dim = sum(clips[v].ln_final.weight.size(0) for v in clips)
+
+        if self.num_projection_vector == 0:
+            self.projection = nn.Identity()
+        else:
+            self.projection = nn.Linear(self.clips_hidden_dim, hidden_state_dim * self.num_projection_vector, bias=True)
+            self.projection.to(dtype=self.dtype)
+            nn.init.normal_(self.projection.weight, std=self.clips_hidden_dim ** -0.5)
+
+        self.clips = clips
+
+        self._move_flag = False
+
+    def move(self):
+        if self._move_flag:
+            return
+
+        def convert_weights(model: nn.Module):
+            """Convert applicable model parameters to fp16"""
+
+            def _convert_weights_to_fp16(l):
+                if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+                    l.weight.data = l.weight.data.type(self.dtype)
+                    if l.bias is not None:
+                        l.bias.data = l.bias.data.type(self.dtype)
+
+                if isinstance(l, nn.MultiheadAttention):
+                    for attr in [
+                        *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
+                        "in_proj_bias",
+                        "bias_k",
+                        "bias_v",
+                    ]:
+                        tensor = getattr(l, attr)
+                        if tensor is not None:
+                            tensor.data = tensor.data.type(self.dtype)
+
+                for name in ["text_projection", "proj"]:
+                    if hasattr(l, name):
+                        attr = getattr(l, name)
+                        if attr is not None:
+                            attr.data = attr.data.type(self.dtype)
+
+            model.apply(_convert_weights_to_fp16)
+
+        for k in self.clips:
+            self.clips[k].to(self.device)
+            convert_weights(self.clips[k])  # fp32 -> self.dtype
+        self._move_flag = True
+
+    def unconditional_embedding(self, batch_size=None):
+        zero = torch.zeros(
+            batch_size,
+            self.clips_hidden_dim,
+            device=self.device,
+            dtype=self.dtype,
+        )
+        if self.num_projection_vector > 0:
+            zero = self.projection(zero).view(batch_size, self.num_projection_vector, -1)
+        return zero
+
+    def convert_embedding(self, z):
+        if self.num_projection_vector > 0:
+            z = self.projection(z.type(self.projection.weight.dtype)).view(len(z), self.num_projection_vector, -1)
+        return z
+
+    def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = self.transform(image)
+
+        with torch.no_grad():
+            embs = []
+            for v in self.clips:
+                x = self.clips[v].encode_image(image)
+                if self.normalize:
+                    x = x / x.norm(p=2, dim=-1, keepdim=True) * (x.size(-1) ** 0.5)
+                    # clip_max only works with normalization
+                    if self.clip_max > 0:
+                        x = x.clamp(-self.clip_max, self.clip_max)
+                embs.append(x)
+
+            z = torch.cat(embs, dim=-1)
+            if self.normalize:
+                z /= z.size(-1) ** 0.5
+
+        if zero_embedding_radio > 0:
+            mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) >= zero_embedding_radio
+            z = z + mask.to(z)
+
+        if self.num_projection_vector > 0:
+            z = self.projection(z).view(len(image), self.num_projection_vector, -1)
+        return z
+
+    def encode(self, image):
+        self.move()
+        return self(image, zero_embedding_radio=self.zero_embedding_radio)