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    UrangDiffusion v3.x - v2.0
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    [Information regarding v3.1 is on "About this version" section]

    UrangDiffusion v3.x (oo-raw-ng Diffusion) is the first UrangDiffusion version that utilize Animagine XL 4.0 as the base.

    The name “Urang” comes from Sundanese, meaning “We/Our/I.” The history behind the name is to make the model not only suitable for me but also for many people. Another reason is that I use many resources (training scripts, dataset collecting scripts, etc.) from other people. It’s unfair to claim this model as “my sole work.”

    Standard Prompting Guidelines

    • Prompting guide:

    • Default negative prompt: lowres, bad ???????, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry

    • Default configuration: Euler a with around 25-30 steps, CFG 5-7, and ENSD set to 31337. Sweet spot is around 27 steps and CFG 6.

    Training Configurations (v3.0)

    [Check about this version for v3.1]

    Finetuned from: Animagine XL 4.0-Zero

    Finetuning:

    • Dataset size: ~1,600 images

    • GPU: 1xA100 80GB

    • Optimizer: AdaFactor

    • Unet Learning Rate: 1.25e-6

    • Text Encoder Learning Rate: N/A (Turned off)

    • Batch Size: 48

    • Gradient Accumulation: 1

    • Warmup steps: 5%

    • Min SNR: 5

    • Epoch: 11

    Special Thanks

    • My co-workers(?) at CagliostroLab for the insights and feedback.

    • Nur Hikari and Vanilla Latte for quality control.

    • Linaqruf, my tutor and role model in AI-generated images, and also the person behind tag ordering.

    License

    UrangDiffusion v1.0-v2.5 falls under the Fair AI Public License 1.0-SD license, while v3.x falls under the CreativeML OpenRAIL++-M license.

    Description

    Using huber loss for better training.

    Using random cropping for better image results.

    Added new characters from new series, updated undertrained characters, and add new characters from existing series.