What is Lora AI wan video train?
Lora AI wan video train is an online service for training Wan 2.2 video LoRA models entirely in your browser. You upload a ZIP file containing at least 10 images, set a trigger word and training parameters, and the platform handles the rest—no local GPU, Python, CUDA, or ComfyUI setup required. The tool learns visual identity from images, such as a character, product, object, or art style, and produces a reusable LoRA adapter for Wan 2.2 image-to-video generation. When training completes, the high-noise and low-noise weights are saved as a pair, ready to load directly into a compatible generator.
Who it's for
- Creators needing consistent character identity — if you want the same face or character to appear reliably across different scenes and motion prompts, this trains a reusable visual reference.
- Product and style designers — anyone who needs a specific object shape or art style to stay stable across multiple video generations will benefit from a focused LoRA.
- Users without local training hardware — if you lack a capable GPU or don't want to manage a training environment, the browser-based workflow removes that entire barrier.
Key features
Browser-based training workflow
Upload a single ZIP file, enter a trigger word, and adjust steps, learning rate, and LoRA rank through a simple form. The platform sends the job to the verified Wan 2.2 image LoRA trainer and tracks its status without any local scripts or setup.
Default settings with transparent pricing
The default configuration uses 1000 steps, a 0.0002 learning rate, and rank 32, costing 300 credits. Steps scale linearly, while higher learning rates and ranks add proportional surcharges. The current total is displayed before you submit.
Paired weight storage
Wan 2.2 returns both high-noise and low-noise weights, and the platform stores both URLs in a single manifest. This prevents the common problem of losing one file and later loading an incomplete pair.
Focused dataset guidance
The service emphasizes that a focused dataset beats a crowded one. Keep one main subject or style per ZIP, and remove duplicates, blur, watermarks, and UI screenshots for the best results.
What stands out
"A focused dataset beats a crowded one."
This principle shapes the entire training philosophy. Instead of promising magic from messy inputs, the platform guides you toward clean, single-subject datasets and recommends starting with default settings. The browser-based approach combined with transparent credit costs makes professional-grade LoRA training accessible to anyone, while the paired-weight storage ensures your trained model is always complete and ready to use.
Worth checking out if…
You want to train a reusable Wan 2.2 video LoRA without investing in local GPU hardware, or if you need consistent character, product, or style identity across multiple video generations. It's also a strong fit if you prefer a guided workflow with clear pricing over complex local setups.
