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

    , 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
  • For more detailed documentation, you can visit my article on Ko-fi (available to supporters only).
• 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.
• 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

Aesthetic tags

Rating tags

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.