---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- diffusers
---

# AnimeBoysXL

**It takes substantial time and efforts to bake models. If you appreciate my models, I would be grateful if you could support me on [Ko-fi](https://ko-fi.com/koolchh) ☕.**

## Features

- ✔️ **Good for inference**: AnimeBoysXL is a flexible model which is good at generating images of anime boys and males-only content in a wide range of styles.
- ✔️ **Good for training**: AnimeBoysXL is suitable for further training, thanks to its neutral style and ability to recognize a great deal of concepts. Feel free to train your own anime boy model/LoRA from AnimeBoysXL.
- ❌ AnimeBoysXL is not optimized for creating anime girls. Please consider using other models for that purpose.

## Inference Guide

- **Prompt**: Use tag-based prompts to describe your subject.
  - Append `, best quality, amazing quality, best aesthetic, absurdres` to the prompt to improve image quality.
  - (*Optional*) Append `, year YYYY` to the prompt to shift the output toward the prevalent style of that year. `YYYY` is a 4 digit year, e.g. `, year 2023`
- **Negative prompt**: Choose from one of the following two presets.
  1. Heavy (*recommended*): `lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts`
  2. Light: `lowres, jpeg artifacts, worst quality, watermark, blurry, bad aesthetic, 1girl, breasts`
  - (*Optional*) Add `, realistic, lips, nose` to the negative prompt if you need a flat anime-like style.
- **VAE**: Make sure you're using [SDXL VAE](https://huggingface.co/stabilityai/sdxl-vae/tree/main).
- **Sampling method, sampling steps and CFG scale**: I find **(Euler a, 28, 5)** good. You are encouraged to experiment with other settings.
- **Width and height**: **832*1216** for portrait, **1024*1024** for square, and **1216*832** for landscape.

## Training Details

AnimeBoysXL is trained from [Stable Diffusion XL Base 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), on ~516k images.

The following tags are attached to the training data to make it easier to steer toward either more aesthetic or more flexible results.

### Quality tags

| tag               | score      |
|-------------------|------------|
| `best quality`    | >= 150     |
| `amazing quality` | [100, 150) |
| `great quality`   | [75, 100)  |
| `normal quality`  | [0, 75)    |
| `bad quality`     | (-5, 0)    |
| `worst quality`   | <= -5      |

### Aesthetic tags

| tag                | score        |
|--------------------|--------------|
| `best aesthetic`   | >= 6.675     |
| `great aesthetic`  | [6.0, 6.675) |
| `normal aesthetic` | [5.0, 6.0)   |
| `bad aesthetic`    | < 5.0        |

### Rating tags

| tag             | rating       |
|-----------------|--------------|
| (None)          | general      |
| `slightly nsfw` | sensitive    |
| `fairly nsfw`   | questionable |
| `very nsfw`     | explicit     |

### Year tags

`year YYYY` where `YYYY` is in the range of [2005, 2023].

### Training configurations

- Hardware: 4 * Nvidia A100 80GB GPUs
- Optimizer: AdaFactor
- Gradient accumulation steps: 8
- Batch size: 4 * 8 * 4 = 128
- Learning rates:
  - 8e-6 for U-Net
  - 5.2e-6 for text encoder 1 (CLIP ViT-L)
  - 4.8e-6 for text encoder 2 (OpenCLIP ViT-bigG)
- Learning rate schedule: constant with 250 warmup steps
- Mixed precision training type: BF16
- Epochs: 20