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Elke

30

Updated: Aug 20, 2026

characternobody

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bf16 SafeTensor

minimaxh3-elke-v01.safetensors

BF16, good balance • 221.86 MB

Verified:

Type
LoRA
Stats

227

30

206

Reviews
Published

Aug 20, 2026

Base Model

MiniMax H3

Training
Steps: 9,000
Epochs: 300
Hash
AutoV2
C2F11350BD
Trigger Words
Elke
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Followers - 327

327

Likes - 98

98

MiniMax H3 is licensed by MiniMax under the MiniMax H3 Community License Agreement. That agreement’s Applicable Territory excludes the European Union, the United Kingdom, the Republic of Korea and the United States of America. Your use of H3 and of any H3 derivative is subject to that agreement and its Acceptable Use Policy.

MiniMax H3

Elke, a fictional character.

Trained on 30 Klein 9b generated images, from an initial JIB mix qwen generated image.

minimax h3

Trained with ai-toolkit, automagic v3 with Contrastive Guidance and training adapter v1; differential guidance scale = 1; rank 24. ai-toolkit configuration attached as minimaxh3-elke-v01.yaml

krea 2

Trained with diffusion-pipe, ProdigyPlusScheduleFree optimizer (https://github.com/tdrussell/diffusion-pipe/pull/483), lokr factor 4, masked loss

ideogram 4

v02

Trained with diffusion-pipe, ProdigyPlusScheduleFree optimizer (https://github.com/tdrussell/diffusion-pipe/pull/483), rank 128, using plain text (non-JSON) captions. config-ideogram4-v02.zip contains training images, mask images, captions, validation images, and diffusion-pipe configuration used.

v01

Trained with ai-toolkit, automagic v3 optimizer, LR 0.00001, weight decay 0.00001, rank 32, with plain text (not JSON) captions. Likeness is slightly higher when using plain text during inference instead of JSON, but JSON still works. The config-ideogram4-v01.zip attachment contains the training images, masks, captions, validation images, and the ai-toolkit configuration.

qwen 2512

Trained with diffusion-pipe, this is a 128-rank lora trained on qwen image 2512, using the ProdigyPlusScheduleFree optimizer (https://github.com/tdrussell/diffusion-pipe/pull/483), on 30 1440x1440 images, using masked training with backgrounds masked to 10%

The model was evaluated every 300 steps (10 repeats of each training image) using FaceNet512 to evaluate the face similarity, using the same settings, seed, etc for inference, calculating the average distance to a subset of 10 of the training images.

The attached training data zip includes: the diffusion pipe config files; the training images, prompts, and masks; the validation images (subset of training images); and the output of MirrorMetrics evaluation of epoch15 vs. the validation set.