by Linaqruf
Open source · 4k downloads · 25 likes
Style Enhancer XL LoRA is an advanced LoRA model designed to elevate anime-style images with exceptional quality and detail. It seamlessly integrates with the Stable Diffusion XL framework, allowing for refined generations through Danbooru tags, which provide precise control over the styles and details to be applied. Whether for expressive portraits, intricate scenes, or specific moods, this model excels in visual enrichment while maintaining artistic coherence. Its standout feature lies in its ability to transform standard renders into polished works, ideal for artists and creators aiming to optimize their visuals. Perfect for projects requiring a professional touch or diverse styles, it stands out for its flexibility and high-end output.
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Style Enhancer XL LoRA is an advanced, high-resolution LoRA (Low-Rank Adaptation) adapter designed to enhance the capabilities of Animagine XL 2.0. This innovative model excels in fine-tuning and refining anime-style images, producing unparalleled quality and detail. It seamlessly integrates with the Stable Diffusion XL framework, and uniquely supports Danbooru tags for precise and creative image generation.
Example tags include face focus, cute, masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck.
Ensure the installation of the latest diffusers library, along with other essential packages:
pip install diffusers --upgrade
pip install transformers accelerate safetensors
The following Python script demonstrates how to utilize the Style Enhancer XL 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/style-enhancer-xl-lora"
lora_filename = "style-enhancer-xl.safetensors"
# 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=0.6)
# 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")