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    UrangDiffusion v3.1 - v1.4
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    [v3.1 is still in further testing. Updates regarding new findings will be updated in the "About this version" section]

    UrangDiffusion v3.1 (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 anatomy, 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 28 steps and CFG 6.

    Training Configurations

    Finetuned from: Animagine XL 4.0 Base (NOT 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: 15

    FAQ

    • Q: Images are sometimes noisy.

    • A: This is a common issue with Animagine XL 4.0 models in general. The base model is trained with only 10 epochs, which lead to the model being undertrained. Unlike Initial N or Initial I model that are trained with more resources.

    • Q: Hires fix model?

    • A: Check out the cover image metadata, you'll find it there.

    • Q: Initial N/Initial I is better.

    • A: Just leave and do not use the model. Simple. No need to announce your departure. Except you're willing to leave a constructive feedback or willing to fund future projects.

    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

    Finetuned from: Animagine XL 3.1

    Pretraining:

    • Dataset size: 34,368 images

    • GPU: 1xA100 80GB

    • Optimizer: AdaFactor

    • Unet Learning Rate: 3.75e-6

    • Text Encoder Learning Rate: 1.875e-6

    • Batch Size: 48

    • Gradient Accumulation: 1

    • Warmup steps: 100 steps

    • Min SNR: 5

    • Epoch: 10 (epoch 9 is used)

    Finetuning:

    • Dataset size: 7,104 images

    • GPU: 1xA100 80GB

    • Optimizer: AdaFactor

    • Unet Learning Rate: 3e-6

    • Text Encoder Learning Rate: - (Train TE set to False)

    • Batch Size: 48

    • Gradient Accumulation: 1

    • Warmup steps: 5%

    • Min SNR: 5

    • Epoch: 10 (epoch 8 is used)

    • Noise Offset: 0.0357

    Added Series

    Wuthering Waves, Zenless Zone Zero, Sewayaki Kitsune no Senko-san and hololiveEN -Justice- have been added to the model.