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Star Guardian Pack (League of Legends)

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bf16 SafeTensor

BF16, good balance • 44.01 MB

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Type

LoRA

Stats

25

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Published

Jun 27, 2026

Base Model

Anima

Hash

AutoV2
CB200FFBEB

Trigger Words

SGXay, purple eyes, animal ears, two-tone hair, pink and purple hair, low ponytail, hair over one eye, asymmetrical bangs, hair ornament, armlet, multicolored elbow fingerless gloves, blue thighhighs, dress, gem, skirt
purple pantyhose
feather cape, dark purple feather scarf
bird feet
human feet
dagger
default creator card background decoration
LoLLoras's Avatar

LoLLoras

License:

Anima

The Anima Model is licensed by CircleStone Labs LLC. Copyright CircleStone Labs LLC. IN NO EVENT SHALL CIRCLESTONE LABS LLC BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.

Built on NVIDIA Cosmos

Ported most my LORAs to Anima, make sure to scroll right to find all the variants.

Current status:

✅ Ahri, Akali, Gwen, Janna, Kai'sa, Lux, Morgana, Orianna, Prestige Syndra, Senna, Sona, Taliyah, Xayah, Redeemed Xayah, Zoe
❌Jinx, Miss Fortune, Neeko, Quinn, Seraphine, Syndra (normal)

The core captions are grouped for each character, and then each separate caption group is optional.


The lora are trained on OneTrainer, 100 epochs (10% warmup steps) at 0.0002LR using ADAMW Cosine, on batch size 4. Takes me <5 minutes per LORA, the model is small so that helps the batch size and makes the training incredibly fast.

This model base is way more accurate than SDXL finetunes, but with the current checkpoints, it does struggle a bit for faces, which tend to be too generic. I've been able to fix the issue by using <segment:face> on SwarmUI, but I wanted to keep the images free of any hires detailer.
Do note that as of June 2026, a lot of upscalers wash the colors out on Swarm for Anima images (UltraSharp, Remacri etc..); the SeedVR2 Upscaler extension doesn't seem to have the issue.

I've tried to provide both old-school captioning and sentence captioning.

Some notes:
-A lot of datasets include the familiars, you can try to gen them but I didn't particularly try very hard to get them to gen properly

-Ahri, half the dataset has no tails, so they're optional

-Akali, somehow the most affected by the face issue, might retrain

-Gwen, small dataset, SGGwen scissors are a thing but they don't gen nicely

-Janna, oldest and worst captioned dataset

-Morgana, the pairs of wings are rough to generate, use (X:2), but no guarantee they'll appear

-Sona, the detached sleeves are actually optional if you only want the elbow gloves

-Taliyah, the same dataset on IL generates the transparency of the twin cape much better, this likely means poor prompting on my end

-Xayah & Redeemed Xayah, mostly based on Wild rift, hence the purple pantyhose for base; human feet/bird feet allow you to choose what you want