Wan 2.2 Image LoRA Trainer API

Wan 2.2 image LoRA Trainer

Wan 2.2 LoRA Trainer is a high-performance custom model training service for the Wan 2.2 text-to-video generation model. Train personalized LoRA (Low-Rank Adaptation) models 10x faster than traditional methods, enabling custom styles, characters, and objects for video generation.

Training Architecture

The trainer leverages Wan 2.2's innovative MoE (Mixture of Experts) architecture, producing two specialized LoRA models:

• high_noise_lora: Optimized for high-noise denoising timesteps, handling initial structure and composition
• low_noise_lora: Optimized for low-noise denoising timesteps, refining details and final output quality

This dual-model approach ensures superior training efficiency and generation quality across all denoising stages.

Training Process

1. Data Upload: Upload a ZIP file containing your training images
2. Automatic Processing: The system automatically processes and optimizes your dataset
3. Dual Model Training: Simultaneously trains both high_noise_lora and low_noise_lora models
4. Model Delivery: Receive two specialized LoRA models ready for video generation