[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.”<h2 id="standard-prompting-guidelines-hlzesdv89">Standard Prompting Guidelines</h2><ul><li>Prompting guide:<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/04b4aacf-0336-4fd5-a221-ea1e29dcf9b5/width=525/04b4aacf-0336-4fd5-a221-ea1e29dcf9b5.jpeg" /></li></ul><ul><li>Default negative prompt: <code>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</code></li><li>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.</li></ul><h2 id="training-configurations-wag59pcf1">Training Configurations</h2>Finetuned from: Animagine XL 4.0 Base (NOT 4.0-Zero)Finetuning:<ul><li>Dataset size: ~1,600 images</li><li>GPU: 1xA100 80GB</li><li>Optimizer: AdaFactor</li><li>Unet Learning Rate: 1.25e-6</li><li>Text Encoder Learning Rate: N/A (Turned off)</li><li>Batch Size: 48</li><li>Gradient Accumulation: 1</li><li>Warmup steps: 5%</li><li>Min SNR: 5</li><li>Epoch: 15</li></ul><h2 id="faq-um1wr3zu2">FAQ</h2><ul><li>Q: Images are sometimes noisy.</li><li>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.</li></ul><ul><li>Q: Hires fix model?</li><li>A: Check out the cover image metadata, you'll find it there.</li></ul><ul><li>Q: Initial N/Initial I is better.</li><li>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.</li></ul><h2 id="special-thanks-dbxwtlfoj">Special Thanks</h2><ul><li>My co-workers(?) at CagliostroLab for the insights and feedback.</li><li>Nur Hikari and Vanilla Latte for quality control.</li><li>Linaqruf, my tutor and role model in AI-generated images, and also the person behind tag ordering.</li></ul><h2 id="license-0mxe0luju">License</h2>UrangDiffusion v1.0-v2.5 falls under the <a target="_blank" rel="ugc" href="https://freedevproject.org/faipl-1.0-sd/">Fair AI Public License 1.0-SD</a> license, while v3.x falls under the <a target="_blank" rel="ugc" href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md">CreativeML OpenRAIL++-M license</a>.

UrangDiffusion v3.1

Training ConfigurationsFinetuned from: Animagine XL 3.1Pretraining:<ul><li>Dataset size: 27,545 images</li><li>GPU: 1xA100 80GB</li><li>Optimizer: AdaFactor</li><li>Unet Learning Rate: 3.75e-6</li><li>Text Encoder Learning Rate: 1.875e-6</li><li>Batch Size: 48</li><li>Gradient Accumulation: 1</li><li>Warmup steps: 100 steps</li><li>Min SNR: 5</li><li>Epoch: 10</li></ul>Finetuning:<ul><li>Dataset size: ~6,800 images</li><li>GPU: 1xA100 80GB</li><li>Optimizer: AdaFactor</li><li>Unet Learning Rate: 2e-6</li><li>Text Encoder Learning Rate: - (Train TE set to False)</li><li>Batch Size: 48</li><li>Gradient Accumulation: 1</li><li>Warmup steps: 5%</li><li>Min SNR: 5</li><li>Epoch: 10</li><li>Noise Offset: 0.0357</li></ul>

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