HunYuan3D AI API: Complete Developer’s Guide

Introduction

HunYuan3D AI’s API provides developers with powerful programmatic access to state-of-the-art 3D model generation capabilities . This comprehensive guide will walk you through everything you need to know to integrate and leverage the API effectively in your applications.

Getting Started

Authentication and Access

Before diving into the API endpoints, you’ll need to set up authentication:

import hunyuan3d

# Initialize API client
client = hunyuan3d.Client(
    api_key="your_api_key",
    environment="production"
)

Basic Configuration

Set up your development environment with these essential configurations:

# Configure API settings
client.configure(
    max_retries=3,
    timeout=300,
    verbose=True
)

Core API Endpoints

Model Generation

The primary endpoint for 3D model generation:

# Generate a 3D model
response = client.generate(
    prompt="modern office chair",
    style="realistic",
    format="glb",
    parameters={
        "polygon_count": 10000,
        "texture_resolution": 2048,
        "optimization_level": "high"
    }
)

Model Manipulation

Endpoints for modifying existing models:

# Modify existing model
modified_model = client.modify(
    model_id="model_xyz",
    modifications={
        "scale": 1.5,
        "rotation": [0, 90, 0],
        "material": "metal"
    }
)

Advanced Features

Batch Processing

For handling multiple models efficiently:

# Batch generation
results = client.batch_generate([
    {"prompt": "vintage lamp", "style": "art_deco"},
    {"prompt": "modern sofa", "style": "minimalist"},
    {"prompt": "wooden table", "style": "rustic"}
])

Real-time Processing

Implementation for real-time model generation:

# Stream generation progress
async for progress in client.generate_with_progress(
    prompt="detailed car model",
    style="realistic"
):
    print(f"Progress: {progress.percentage}%")

Integration Examples

Blender Integration

Example of integrating with Blender :

import bpy
import hunyuan3d

def generate_and_import(prompt):
    # Generate model
    model = client.generate(prompt=prompt)
    
    # Import into Blender
    bpy.ops.import_scene.gltf(
        filepath=model.get_path()
    )

ComfyUI Integration

Integration with ComfyUI workflow:

# ComfyUI node setup
class Hunyuan3DNode:
    def __init__(self):
        self.client = hunyuan3d.Client()
    
    def process(self, prompt, parameters):
        return self.client.generate(
            prompt=prompt,
            **parameters
        )

Best Practices

Error Handling

Implement robust error handling:

try:
    model = client.generate(prompt="complex scene")
except hunyuan3d.APIError as e:
    logger.error(f"API Error: {e.message}")
except hunyuan3d.ValidationError as e:
    logger.error(f"Validation Error: {e.details}")

Rate Limiting

Manage API requests efficiently:

from ratelimit import limits, sleep_and_retry

@sleep_and_retry
@limits(calls=100, period=60)
def rate_limited_generate(prompt):
    return client.generate(prompt=prompt)

Performance Optimization

Caching Strategy

Implement efficient caching:

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_generate(prompt, style):
    return client.generate(
        prompt=prompt,
        style=style
    )

Resource Management

Optimize resource usage:

# Configure resource limits
client.set_resource_limits(
    max_concurrent_requests=5,
    max_memory_usage="4GB",
    cleanup_interval=300
)

Security Considerations

API Key Management

Secure handling of API credentials:

import os
from dotenv import load_dotenv

load_dotenv()
api_key = os.getenv('HUNYUAN3D_API_KEY')
client = hunyuan3d.Client(api_key=api_key)

Request Validation

Implement input validation:

def validate_request(prompt, parameters):
    if not isinstance(prompt, str):
        raise ValueError("Prompt must be a string")
    if len(prompt) > 1000:
        raise ValueError("Prompt too long")
    # Additional validation logic

Monitoring and Analytics

Performance Tracking

Monitor API usage and performance:

# Initialize monitoring
metrics = client.init_metrics()

# Track API calls
with metrics.track_request():
    model = client.generate(prompt="test model")

Usage Analytics

Implement usage tracking:

# Track API usage
analytics = client.get_analytics()
print(f"Total requests: {analytics.total_requests}")
print(f"Average response time: {analytics.avg_response_time}ms")

Conclusion

The HunYuan3D AI API provides a powerful and flexible platform for integrating 3D model generation into your applications. By following this guide and implementing the provided examples, you can effectively leverage the API’s capabilities while maintaining optimal performance and security.

Remember to regularly check the official documentation for updates and new features, and join the community forums for support and best practices sharing.

References

1. Official API Documentation
2. HunYuan3D GitHub Repository
3. Community Forums
4. Integration Guides
5. Security Best Practices