名探偵プリキュア！の「キュアアルカナ・シャドウ」の、Anima用のLoraです。
杖の長さと真っ直ぐさがあまり安定しません。Animaに未だ慣れてい無いため、未だサンプル画像も適当です。
トリガーワード以外のタグを使用すると、画風が固定されやすいかも。
ウェイト：0.6-1.0
着替えさせる場合：0.3辺りとタグ併用
トリガーワード: cure arcana shadow, meitantei precure!
頭部: blonde hair, pink hair, gradient hair, multicolored hair, very long hair, antenna hair, hair intakes, circlet, hair bow, veil, pink eyes, earring,
上半身: black dress, black capelet, black bow, frills, jewelry, white wrist cuffs,
下半身: black footwear, black thighhigh, high heels,
オプション: staff, holding staff, purple nails,
This is a Lora for Cure Arcana Shadow from Meitantei Precure!, made for Anima.
The length and straightness of the staff tend to be unstable. Since I'm not yet used to Anima, the sample images are still rough.
Using tags other than the trigger words may cause the art style to become fixed.
Weight：0.6-1.0
If changing clothes: use around 0.3 along with the tag
Trigger words: cure arcana shadow, meitantei precure!
Head: blonde hair, pink hair, gradient hair, multicolored hair, very long hair, antenna hair, hair intakes, circlet, hair bow, veil, pink eyes, earring,
Upper body black dress, black capelet, black bow, frills, jewelry, white wrist cuffs,
Lower body: black footwear, black thighhigh, high heels,
Options: staff, holding staff, purple nails,
The CircleStone Model is licensed by CircleStone Labs LLC under the CircleStone Non-Commercial License. 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.
circlestone-labs-non-commercial-license
Description
FAQ
Comments (6)
Mind sharing amount of training images and exact parameters like learning rate etc.?
All 98 official images. However, 91 of them are from anime footage, and 81 of those are upper body/face only. So I split the dataset into 3 groups: "Upper body/Face", "Full body/Above knee", and "Standing art/Non-anime footage".
With batch size = 2:
- "Upper body/Face" repeats 8: 81×8÷2 = 324
- "Full body/Above knee" repeats 20: 10×20÷2 = 100
- "Standing art/Non-anime footage" repeats 25: 7×25÷2 ≒ 88
- 1 epoch: ~512 steps total
- 10 epochs: ~5100 steps
Training time: ~4h 30min on RTX 3090 Ti
Optimizer used:
https://github.com/muooon/EmoSens
https://civitai.com/articles/24407/emosens-231
Parameters:
```
resolution = "1024,1024"
enable_bucket = true
min_bucket_reso = 320
bucket_reso_steps = 64
caption_extension = ".txt"
caption_dropout_rate = 0.05
shuffle_caption = true
keep_tokens = 2
max_train_epochs = 10
train_batch_size = 2
learning_rate = 1
lr_scheduler = "constant"
max_grad_norm = 1.0
network_module = "networks.lora_anima"
network_dim = 32
network_alpha = 32
optimizer_type = "optimizer.emosens.EmoSens"
mixed_precision = "bf16"
save_precision = "fp16"
loss_type = "l2"
```
Note: learning_rate=1 is the recommended setting for EmoSens, as it handles LR adjustment internally.
Note: Memory optimization settings (gradient_checkpointing, flash_attn, cache_latents, etc.) are omitted as they don't affect training results.
@aa4666lo Sorry for dumb questions, but how do you install EmoSense? Doesn't seem to be included by default in sd-scripts
@iamjustheretodow6140
Download the zip from https://github.com/muooon/EmoSens/releases/tag/EmoVoid and place the included "optimizer" folder into your sd-scripts directory. It should contain "emosens.py" inside.
The path should look like: sd-scripts/optimizer/emosens.py
@aa4666lo Got it all setup, seems to be working. Sorry for asking so many questions, but what GPU do you use and how many iterations a second do you get? Thinking about getting a new one.
I have a AMD GPU and it's clearly not optimized for this type of thing, I had to ask Gemini-cli to edit the training script to even get it to run without running out of VRAM despite having 16GBs and now that it does I'm only getting like 32.94s/it. My plan is to just leave it running over night for a few days lol
@iamjustheretodow6140
RTX 3090 Ti with 65% power limit. At 512px training resolution I get ~1.3s/it, and at 1024px ~2.5-3.2s/it.
Even the older RTX 3000 series can manage this kind of speed, so yeah, I really think AMD GPUs just aren't suited for training. From my own testing, training at 512px didn't show as much quality loss as I expected. I ended up going with 1024px mostly for peace of mind, but for the current anima-preview, 512px should work fine.
32.94s/it is... that's rough. If you're buying a GPU for image generation, NVIDIA is definitely the easier path lol
Here's a snippet from my sd-scripts training log for reference:
```
2026-02-21 06:38:02 INFO epoch is incremented. current_epoch: 8, epoch: 9 train_util.py:784
steps: 90%|█████████████████████████████████████████████████████████████████████████▊ | 4878/5420 [3:23:45<22:38, 2.51s/it, avr_loss=0.0334]
saving checkpoint:
epoch 10/10
2026-02-21 07:00:36 INFO epoch is incremented. current_epoch: 9, epoch: 10 train_util.py:784
steps: 100%|██████████████████████████████████████████████████████████████████████████████████| 5420/5420 [3:46:20<00:00, 2.51s/it, avr_loss=0.0324]
```