AnimeBoysXL-v1.0 / README.md
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metadata
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 ☕.

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.
  • 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, 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