AnimeBoysXL-v2.0 / README.md
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---
license: openrail++
tags:
- text-to-image
- stable-diffusion
- diffusers
---
# AnimeBoysXL v2.0
**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 v2.0 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 v2.0 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 v2.0 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.
- Tag ordering matters. It is highly recommended to structure your prompt with the following templates:
```
1boy, male focus, character name, series name, anything else you'd like to describe
```
```
2boys, male focus, multiple boys, character name(s), series name, anything else you'd like to describe
```
- 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 face.
- **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.
## 🧨Diffusers Example Usage
```python
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("Koolchh/AnimeBoysXL-v2.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
prompt = "1boy, male focus, best quality, amazing quality, best aesthetic, absurdres"
negative_prompt = "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"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=5,
num_inference_steps=28
).images[0]
```
## Training Details
AnimeBoysXL v2.0 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 |
|-----------------|--------------|
| `sfw` | 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)