This adapter is trained to improve anime character generation quality
across 5 artistic styles (impressionist, soft-focus, oil painting, sketch, pastel).

The model learns style-specific features from Danbooru anime images,
applied to the UNet attention layers (to_k, to_v, to_q, to_out.0).

Example Output

Training Configuration

Base model: runwayml/stable-diffusion-v1-5
Method: LoRA (PEFT)
Target modules: to_k, to_v, to_q, to_out.0 (Attention Linear layers only)
LoRA rank: 8
LoRA alpha: 32.0
Learning rate: 1e-4
Batch size: 2
Epochs: 10
Final loss: 0.146112
GPU: Colab T4 (16 GB VRAM)

Dataset

Danbooru anime images collected and classified into 5 styles:

Style	Images
impressionist_style	~60
soft_focus_landscape	~60
oil_painting_aesthetic	~60
sketch_aesthetic	~60
pastel_softness	~60

Total: ~300 images

Usage

python

import torch
from diffusers import StableDiffusionPipeline
from peft import PeftModel

base = "runwayml/stable-diffusion-v1-5"
adapter = "Shion1124/anime-character-lora_v1.5"

pipe = StableDiffusionPipeline.from_pretrained(base, torch_dtype=torch.float16)
pipe.unet = PeftModel.from_pretrained(pipe.unet, adapter, adapter_name="anime_lora")
pipe = pipe.to("cuda")

image = pipe(
    prompt="1girl, anime character, watercolor style, masterpiece, high quality",
    negative_prompt="low quality, blurry, distorted, nsfw",
    num_inference_steps=20,
    guidance_scale=7.5,
    height=512,
    width=512,
    generator=torch.Generator(device="cuda").manual_seed(42)
).images[0]
image.save("output.png")

Recommended Prompts

Anime character:

1girl, anime character, detailed beautiful face, long hair,
watercolor painting style, soft colors, bokeh background,
masterpiece, best quality, high quality, intricate details

Negative prompt:

low quality, worst quality, blurry, distorted, watermark,
error, nsfw, extra limbs, missing limbs, ugly, bad anatomy

Parameter	Value
`num_inference_steps`	20
`guidance_scale`	7.5
`height / width`	512 × 512

Sources & Terms (IMPORTANT)

Training data: Danbooru (https://danbooru.donmai.us/)

Dataset License: CC0 (Public Domain). Images sourced from Danbooru under CC0 terms.
Compliance: Users must comply with the base model's original license terms (OpenRAIL-M).

License

Component	License
Stable Diffusion v1.5	OpenRAIL-M
LoRA adapter (this repo)	Apache 2.0
Training data (Danbooru)	CC0

References

Ho et al. (2020) - Denoising Diffusion Probabilistic Models - arXiv:2006.11239
Rombach et al. (2022) - High-Resolution Image Synthesis with Latent Diffusion Models - arXiv:2112.10752
Hu et al. (2021) - LoRA: Low-Rank Adaptation of Large Language Models - arXiv:2106.09685