Neta Lumina v1.0 for diffusers library

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Introduction

Neta Lumina is a high‑quality anime‑style image‑generation model developed by Neta.art Lab.
Building on the open‑source Lumina‑Image‑2.0 released by the Alpha‑VLLM team at Shanghai AI Laboratory, we fine‑tuned the model with a vast corpus of high‑quality anime images and multilingual tag data. The preliminary result is a compelling model with powerful comprehension and interpretation abilities (thanks to Gemma text encoder), ideal for illustration, posters, storyboards, character design, and more.

Key Features

Optimized for diverse creative scenarios such as Furry, Guofeng (traditional‑Chinese aesthetics), pets, etc.
Wide coverage of characters and styles, from popular to niche concepts. (Still support danbooru tags!)
Accurate natural‑language understanding with excellent adherence to complex prompts.
Native multilingual support, with Chinese, English, and Japanese recommended first.

Model Versions

For models in alpha tests, requst access at https://huggingface.co/neta-art/NetaLumina_Alpha if you are interested. We will keep updating.

neta-lumina-v1.0

Official Release: overall best performance

neta-lumina-beta-0624-raw (archived)

Primary Goal: General knowledge and anime‑style optimization
Data Set: >13 million anime‑style images
>46,000 A100 Hours
Higher upper limit, suitable for pro users. Check Neta Lumina Prompt Book for better results.

neta-lumina-beta-0624-aes-experimental (archived)

First beta release candidate
Primary Goal: Enhanced aesthetics, pose accuracy, and scene detail
Data Set: Hundreds of thousands of handpicked high‑quality anime images (fine‑tuned on an older version of raw model)
User-friendly, suitable for most people.

How  to  Use

Try it at Hugging Face playground

Or use it with diffusers:

import torch
from diffusers import Lumina2Pipeline

pipe = Lumina2Pipeline.from_pretrained("VirtualAddressExtension/Neta-Lumina-v1.0-diffusers", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

prompt = "You are an assistant designed to generate anime images based on textual prompts. <Prompt Start> neta, @quasarcake, 1girl, solo, 1girl,solo,bangs,black hair,purple eyes,pink hair,purple hair,multicolored hair,virtual youtuber,hair bun,streaked hair,double bun, school uniform, white shirt, pleated skirt, gentle smile, looking at viewer, sitting, upper body, close-up, soft lighting, depth of field, cherry blossom background, warm lighting, best quality"
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=4.0,
    num_inference_steps=50,
    cfg_trunc_ratio=0.25,
    cfg_normalization=True,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("lumina_demo.png")

Prompt Book

Detailed prompt guidelines: Neta Lumina Prompt Book

Community

Discord: https://discord.com/invite/TTTGccjbEa
QQ group: 1039442542

Roadmap

Model

Continous base‑model training to raise reasoning capability.
Aesthetic‑dataset iteration to improve anatomy, background richness, and overall appealness.
Smarter, more versatile tagging tools to lower the creative barrier.

Ecosystem

LoRA training tutorials and components
- Experienced users may already fine‑tune via Lumina‑Image‑2.0’s open code.
Development of advanced control / style‑consistency features (e.g., Omini Control). Call for Collaboration!

License & Disclaimer

Neta Lumina is released under Apache License 2.0

Participants & Contributors

Special thanks to the Alpha‑VLLM team for open‑sourcing Lumina‑Image‑2.0
Model development: Neta.art Lab (Civitai)
- Core Trainer: li_li Civitai ・ Hugging Face
Partners
- nebulae: Civitai ・ Hugging Face
- 生姜: Hugging Face
- 孙一
narugo1992 & deepghs: open datasets, processing tools, and models
Naifu trainer at Mikubill

Community Contributors

Evaluators & developers: 二小姐, spawner, Rnglg2
Other contributors: 沉迷摸鱼, poi, AshenWitch, 十分无奈, GHOSTLX, wenaka, iiiiii, 年糕特工队, 恩匹希, 奶冻, mumu, yizyin, smile, Yang, 古神, 灵之药, LyloGummy, 雪时

Appendix & Resources

TeaCache: https://github.com/spawner1145/CUI-Lumina2-TeaCache
Advanced samplers & TeaCache guide (by spawner): https://docs.qq.com/doc/DZEFKb1ZrZVZiUmxw?nlc=1
Neta Lumina ComfyUI Manual (in Chinese): https://docs.qq.com/doc/DZEVQZFdtaERPdXVh

VirtualAddressExtension/Neta-Lumina-v1.0-diffusers