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 ☕.
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
to the prompt to improve image quality., best quality, amazing quality, best aesthetic, absurdres
- (Optional) Append
to the prompt to shift the output toward the prevalent style of that year., year YYYY
YYYY
is a 4 digit year, e.g., year 2023
- For more detailed documentation, you can visit my article on Ko-fi (available to supporters only).
- Tag ordering matters. It is highly recommended to structure your prompt with the following templates:
- Negative prompt: Choose from one of the following two presets.
- 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
- Light:
lowres, jpeg artifacts, worst quality, watermark, blurry, bad aesthetic, 1girl, breasts
- (Optional) Add
to the negative prompt if you need a flat anime-like style face., realistic, lips, nose
- VAE: Make sure you're using SDXL VAE.
- 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
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, shirt, solo, looking at viewer, smile, black hair, brown eyes, short hair, 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, 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)
Changes from v1.0
- Train with tag ordering.
- Add
sfw
rating tag. - More epochs on the questionable and explicit rating subset.
- FP16 mixed-precision training for final epochs.
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