Anime Detailer Xl Lora

🚀 Anime Detailer XL LoRA

Anime Detailer XL LoRA is a state - of - the - art LoRA adapter that works with Animagine XL 2.0, enabling users to adjust the detail level in generated anime - style images.

🚀 Quick Start

Anime Detailer XL LoRA is a cutting - edge LoRA adapter designed to work in tandem with Animagine XL 2.0. It allows users to control the level of detail in generated anime - style images through a concept slider.

✨ Features

• Concept Modulation: Specializes in concept modulation, enabling users to adjust the detail level in anime - style images.
• Versatile Artistic Expression: Allows for a wide range of artistic expressions, from highly detailed to more abstract anime imagery.

📦 Installation

Ensure the installation of the latest diffusers library, along with other essential packages:

pip install diffusers --upgrade
pip install transformers accelerate safetensors

💻 Usage Examples

Basic Usage

The following Python script demonstrates how to utilize the LoRA with Animagine XL 2.0. The default scheduler is EulerAncestralDiscreteScheduler, but it can be explicitly defined for clarity.

import torch
from diffusers import (
    StableDiffusionXLPipeline, 
    EulerAncestralDiscreteScheduler,
    AutoencoderKL
)

# Initialize LoRA model and weights
lora_model_id = "Linaqruf/anime-detailer-xl-lora"
lora_filename = "anime-detailer-xl.safetensors"
lora_scale_slider = 2 # -2 for less detailed result

# Load VAE component
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", 
    torch_dtype=torch.float16
)

# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "Linaqruf/animagine-xl-2.0", 
    vae=vae,
    torch_dtype=torch.float16, 
    use_safetensors=True, 
    variant="fp16"
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')

# Load and fuse LoRA weights
pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
pipe.fuse_lora(lora_scale=lora_scale_slider)

# Define prompts and generate image
prompt = "face focus, cute, masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck"
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"

image = pipe(
    prompt, 
    negative_prompt=negative_prompt, 
    width=1024,
    height=1024,
    guidance_scale=12,
    num_inference_steps=50
).images[0]

# Unfuse LoRA before saving the image
pipe.unfuse_lora()
image.save("anime_girl.png")

📚 Documentation

Model Details

Acknowledgements

Our project has been enriched by the following significant works:

• Erasing Concepts from Diffusion Models by Rohit Gandikota et al.
• LECO by p1atdev.
• AI Toolkit by Ostris.

📄 License

This project is under the CreativeML Open RAIL++ - M License.