Aozora-XL: A V-Prediction SDXL Model

Aozora-XL is a v-prediction model based on NoobAI v-pred, fine-tuned for improved stability and coherence. It uses a custom training script that allows full/partial fine-tuning on a 12GB consumer GPU, such as an RTX 3060. The training script is available on GitHub at Aozora_SDXL_Training for community use.

• Never merged

• No internally merged loras

Version 0.15 Updates

This version builds on 0.1 by addressing specific issues in the v-prediction setup. It was trained on the v0.1 base to restore vibrant colors and reduce the slight whitewash effect present in earlier releases. Additional fine-tuning focused on fixing common v-prediction problems, such as inconsistencies in scene composition and detail rendering. It used a dataset of ~50,000 images consisting of visual novel and anime content with deep colors, trained for 5 epochs. Settings included:

- Base Model: Aozora V0.1

- Max Train Steps: 250000

- Gradient Accumulation Steps: ??

- Mixed Precision: bfloat16

- UNET Learning Rate: 8e-07

- LR Scheduler: Cosine with 10% warmup

- Features: Min-SNR Gamma (corrected variant, gamma 5.0), Zero Terminal SNR, IP Noise Gamma (0.1), Residual Shifting, Conditional Dropout (prob 0.1)

These changes result in better color fidelity and more reliable outputs across various prompts.

- Note: All preview images where generated without any detailers or enhancers to show base capabilities

Version 0.1 Overview

The initial release (v0.1 alpha) was a proof-of-concept, trained for 10 epochs on a dataset of ~18,500 images (50% ZZZ characters up to version 2.0, 50% top-rated Danbooru images). It maintains traits from the base model (NoobAI-XL/NAI-XL V-Pred 1.0) while showing gains in stability due to the training approach.

Project Goals

- Provide a GUI-based training script to enable SDXL fine-tuning on consumer hardware.

- Continue developing Aozora-XL into a stable, controllable model through ongoing training on diverse datasets.

Training Method

The method optimizes efficiency by training ~92% of the UNet. It includes adaptive Min-SNR gamma weighting for v-prediction stability and custom learning rate schedules.

Training Specs:

- Hardware: 1x NVIDIA RTX 3060 (12GB VRAM usage: ~11.8 GB)

- Optimizer: Adafactor

- Batch Size: 1 with ?? Gradient Accumulation Steps

- UNet Params Trained: 2.3B

Recommended Settings

- Positive Prompt: very awa, masterpiece, best quality

- Negative Prompt: Optional; try (worst quality, low quality) if needed

- Sampler: DPM++ 3M SDE GPU or Euler (Euler for line art, SDE for details like hands/feet)

- Scheduler: SGM Uniform or Normal

- Steps: 25-35

- CFG Scale: 3-5 (works well at low values)

- Resolution: 1024x1024 or similar (up to 1152x1152)

- Hires. Fix: Use with upscalers like RealESRGEN at ~0.35 denoise

Experiment with settings, as v-prediction models can vary by system.

License

This model follows the license of its base, NoobAI-XL. Review and comply with those terms.