AnimeBoysXL v1.0
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Features
- ✔️ Good for inference: AnimeBoysXL v1.0 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 v1.0 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 v1.0 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.
- Append
, best quality, amazing quality, best aesthetic, absurdresto the prompt to improve image quality. - (Optional) Append
, year YYYYto the prompt to shift the output toward the prevalent style of that year.YYYYis a 4 digit year, e.g., year 2023
- Append
- 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
, realistic, lips, noseto the negative prompt if you need a flat anime-like style face.
- Heavy (recommended):
- 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-v1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
prompt = "1boy, solo, male focus, hoodie, grey hoodie, hood, hood up, close-up, sleepy, short hair, bara, undercut, stubble, black hair, green eyes, parted lips, white background, simple background, best quality, amazing quality, best aesthetic, absurdres, year 2023"
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 v1.0 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 |
|---|---|
| (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