25
Euler a
6
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, displeasing
https://civitai.com/models/537384?modelVersionId=690340
UrangDiffusion v1.3 (oo-raw-ng Diffusion) is an updated version of UrangDiffusion 1.2. This version provides refreshed dataset, improvements over the last iteration, training parameter correction, and some characters optimization.
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.”
The model went through two steps of training: pretraining and finetuning. Pretraining is to make the model learn new things, while finetuning ensures the images produced by the model are decent (A.K.A. having a standard style) without mentioning style in the prompt.
Standard Prompting Guidelines The model is finetuned from Animagine XL 3.1. However, there is a little bit changes on dataset captioning, therefore there is some different default prompt used:
Default prompt: 1girl/1boy, character name, from what series, everything else in any order, masterpiece, best quality, amazing quality, very aesthetic
Default negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, displeasing
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 7.
Training Configurations Finetuned from: Animagine XL 3.1
Pretraining:
Dataset size: 27,545 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
Finetuning:
Dataset size: ~6,800 images
GPU: 1xA100 80GB
Optimizer: AdaFactor
Unet Learning Rate: 2e-6
Text Encoder Learning Rate: - (Train TE set to False)
Batch Size: 48
Gradient Accumulation: 1
Warmup steps: 5%
Min SNR: 5
Epoch: 10
Noise Offset: 0.0357
Added Series Wuthering Waves, Zenless Zone Zero and hololiveEN -Justice- have been added to the model.
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.
License UrangDiffusion falls under the Fair AI Public License 1.0-SD license.
