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Anime Style [WAN 2.2 I2V]

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Updated: Dec 21, 2025

styleanime

Verified:

SafeTensor

Type

LoRA

Stats

1,030

0

Reviews

Published

Dec 19, 2025

Base Model

Wan Video 2.2 I2V-A14B

Training

Steps: 3,500

Trigger Words

An1meStyl3, AnimeStyle

Hash

AutoV2
E03B335D8B

Creates videos in a modern HD anime style; useful for maintaining this style when a prompt or model has a tendency to insert realism or a 3d animation aesthetic.

The primary trigger is "An1meStyl3," but it is recommended to also include the tag "AnimeStyle" alongside it.

Version 1 Notes

WAN 2.2 appears to have a strong bias towards realistic generations, and training this LoRA with more than 2000 steps resulted in overfit and yielded a large amount of noise, even with 6 low-noise steps. As a result, more consistent generation can be achieved by bumping the model strength up to 1.2 or adding "((realistic))" to your negative prompt. In the event you are using one or more concept/motion/character LoRAs with a bias towards realism, use a stronger negative like "(((realistic))), ((photograph))"

This is intended to be a general-use anime LoRA, so it was trained from stills on a diverse set of shows that have a common general art style, rather than fixating on a particular show or creator, so that it doesn't closely resemble any particular illustrator's signature style or creative choices.

Version 2 Notes

From all tests, this version appears significantly more stable using the standard 4-step WAN 2.2 I2V workflow using any type of concept or motion LoRA without introducing unwanted noise or realism. The same negatives are recommended, but 4 steps with a strength of 1.0 should be sufficient.

Version Notes:

  • Version 1: Helps maintain style on characters; suffers quality loss when doing advanced motion or dramatic camera movement. Trained on an image set of images

  • Version 2: Helps maintain style on characters and background scene; more traditional character movement as seen in Japanese animation. Greater compatibility with concept and transition LoRAs without introducing style loss. Trained on a set of images and a set of video clips.