Technology Behind Multiple Angles: LoRA & 3D AI

Introduction

Multiple Angles represents a significant advancement in AI-powered image generation. In this article, we'll explore the cutting-edge technologies that make our multi-angle image generation possible.

The Foundation: Qwen-Image-Edit-2511

Our system is built on Qwen-Image-Edit-2511, a powerful image editing model developed by Alibaba's Qwen team. This model excels at understanding and manipulating images based on textual instructions.

Why Qwen-Image-Edit?

• Strong image understanding capabilities
• Precise editing control through natural language
• High-quality output generation
• Robust architecture suitable for fine-tuning

LoRA: Low-Rank Adaptation

What is LoRA?

LoRA (Low-Rank Adaptation) is a fine-tuning technique that allows us to adapt large pre-trained models for specific tasks without modifying all the model's parameters.

Instead of updating billions of parameters, LoRA introduces small, trainable matrices that capture task-specific knowledge. This approach offers several advantages:

1. Efficiency: Much smaller storage footprint
2. Speed: Faster training and inference
3. Quality: Comparable or better results than full fine-tuning
4. Composability: Can be combined with other LoRAs

Our LoRA Implementation

For Multiple Angles, we developed a specialized LoRA that:

• Understands 96 distinct camera positions
• Maintains 3D consistency across views
• Preserves subject identity from different angles
• Responds to natural language camera descriptions

Gaussian Splatting: The 3D Secret

Understanding Gaussian Splatting

Gaussian Splatting is a revolutionary 3D representation technique that uses millions of 3D Gaussians to represent scenes. Unlike traditional mesh-based or NeRF-based approaches, Gaussian Splatting offers:

• Real-time rendering capabilities
• High visual quality
• Efficient memory usage
• Fast training from images

How We Use Gaussian Splatting

Our training data was generated using Gaussian Splatting technology:

1. 3D Scene Reconstruction: We reconstructed thousands of 3D scenes
2. Multi-View Rendering: Generated consistent images from 96 camera positions
3. Training Pairs: Created source-target image pairs for LoRA training

This approach ensures that our model learns true 3D-consistent transformations, not just 2D image manipulations.

Training Process

Data Collection

We curated a diverse dataset of:

• 3,000+ high-quality subjects
• Varied object categories (products, characters, vehicles, etc.)
• Multiple lighting conditions
• Various background types

Training Configuration

Quality Assurance

Each training sample undergoes:

1. Consistency checking - Ensuring 3D accuracy
2. Quality filtering - Removing artifacts
3. Diversity validation - Maintaining dataset balance

The Prompt System

Camera Description Format

Our model uses a structured prompt format:

<sks> [azimuth] [elevation] [distance]

Examples:

• <sks> front view eye-level shot medium shot
• <sks> right side view high-angle shot close-up
• <sks> back view low-angle shot wide shot

Why This Format?

This structured approach allows:

• Precise control over camera positioning
• Natural language understanding
• Consistent results across generations
• Easy integration into workflows

Comparison with Other Approaches

vs. Traditional 3D Modeling

vs. Other AI Methods

Future Developments

We're continuously improving Multiple Angles:

• Higher resolution support
• More camera positions
• Video generation capabilities
• Custom angle inputs

Conclusion

Multiple Angles combines state-of-the-art AI technologies—LoRA fine-tuning, Gaussian Splatting data, and the powerful Qwen-Image-Edit model—to deliver unprecedented control over multi-angle image generation.

Our commitment to quality and innovation drives us to continually improve and expand the capabilities of this technology.

Experience Multiple Angles today →