ArtiWaifu Diffusion

【https://civitai.com/models/435207/artiwaifu-diffusion】

AWA Diffusion We have released the ArtiWaifu Diffusion model, designed to generate aesthetically pleasing and faithfully restored anime-style illustrations.

The AWA Diffusion is an iteration of the Stable Diffusion XL model, mastering over 6000 artistic styles and more than 4000 anime characters, generating images through trigger words.

As a specialized image generation model for anime, it excels in producing high-quality anime images, especially in generating images with highly recognizable styles and characters while maintaining a consistently high-quality aesthetic expression.

Model Details

The AWA Diffusion model is fine-tuned from Stable Diffusion XL, with a selected dataset of 1.5M high-quality anime images, covering a wide range of both popular and niche anime concepts up to April 15, 2024. AWA Diffusion employs our most advanced training strategies, enabling users to easily induce the model to generate images of specific characters or styles while maintaining high image quality and aesthetic expression.

Model Information

Developed by: Euge Funded by: Neta.art Model type: Generative text-to-image model Finetuned from model: SDXL 1.0 Base License: Fair AI Public License 1.0-SD Usage Guide

This guide will (i) introduce the model's recommended usage methods and prompt writing strategies, aiming to provide suggestions for generation, and (ii) serve as a reference document for model usage, detailing the writing patterns and strategies for trigger words, quality tags, rating tags, style tags, and character tags.

Basic Usage

CFG scale: 5-11 Resolution: Area (= width x height) around 1024x1024. Not lower than 256x256, and resolutions where both length and width are multiples of 32. Sampling method: Euler A (50+ steps) or DPM++ 2M Karras (~35 steps) Due to the special training method, AWA's optimal inference step count are higher than regular values. As the inference steps increase, the quality of the generated images can continue to improve...

❓ Question: Why not use the standard SDXL resolution?

💡 Answer: Because the bucketing algorithm used in training does not adhere to a fixed set of buckets. Although this does not conform to positional encoding, we have not observed any adverse effects.