---
base_model:
- Alpha-VLLM/Lumina-Image-2.0
license: other
license_name: fair-ai-public-license-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
---
[中文版模型说明](https://huggingface.co/neta-art/Neta-Lumina/blob/main/README-ZH.md)
# 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-beta-0624-raw
- **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**](https://nieta-art.feishu.cn/wiki/RVBgwvzBqiCvQ7kOMm1cM6NdnNc) for better results.
### neta-lumina-beta-0624-aes
- 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](https://huggingface.co/spaces/neta-art/NetaLumina_T2I_Playground)
## ComfyUI
Neta Lumina is built on the **Lumina2 Diffusion Transformer (DiT)** framework, please follow these steps precisely.
### Environment Requirements
Currently Neta Lumina runs only on ComfyUI:
- Latest ComfyUI installation
- ≥ 8 GB VRAM
### Downloads & Installation
**Original (component) release**
1. **Neta Lumina-Beta**
- Download link: https://huggingface.co/neta-art/Neta-Lumina/blob/main/neta-lumina-beta-0624.pth
- Save path: `ComfyUI/models/unet/`
2. **Text Encoder (Gemma-2B)**
- Download link:https://huggingface.co/neta-art/Neta-Lumina/resolve/main/gemma_2_2b_fp16.safetensors
- Save path: `ComfyUI/models/text_encoders/`
3. **VAE Model (16-Channel FLUX VAE)**
- Download link: https://huggingface.co/neta-art/Neta-Lumina/resolve/main/ae.safetensors
- Save path: `ComfyUI/models/vae/`
**Workflow**: load [`lumina_workflow.json`](https://huggingface.co/neta-art/NetaLumina_Alpha/blob/main/lumina_workflow.json) in ComfyUI.
- `UNETLoader` – loads the `.pth`
- `VAELoader` – loads `ae.safetensors`
- `CLIPLoader` – loads `gemma_2_2b_fp16.safetensors`
- `Text Encoder` – connects positive /negative prompts to K Sampler
**Simple merged release**
Download [`neta-lumina-beta-0624-all-in-one.safetensors`](https://huggingface.co/neta-art/Neta-Lumina/tree/main),
`md5sum = dca54fef3c64e942c1a62a741c4f9d8a`,
you may use ComfyUI’s simple checkpoint loader workflow.
### Recommended Settings
- **Sampler**: `res_multistep`
- **Scheduler**: `linear_quadratic`
- **Steps**: 30
- **CFG (guidance)**: 4 – 5.5
- **EmptySD3LatentImage resolution**: 1024 × 1024, 768 × 1532, or 968 × 1322
# Prompt Book
Detailed prompt guidelines: [**Neta Lumina Prompt Book**](https://nieta-art.feishu.cn/wiki/RVBgwvzBqiCvQ7kOMm1cM6NdnNc)
# Community
- Discord: https://discord.com/invite/TTTGccjbEa
- QQ group: 785779037
# 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](https://arxiv.org/pdf/2411.15098)). [**Call for Collaboration!**](https://discord.com/invite/TTTGccjbEa)
# License & Disclaimer
- Neta Lumina is released under the [**Fair AI Public License 1.0‑SD**](https://freedevproject.org/faipl-1.0-sd/)
- Any modifications, merges, or derivative models must themselves be open‑sourced.
# 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](https://civitai.com/user/li_li) ・ [Hugging Face](https://huggingface.co/heziiiii)
- **Partners**
- **nebulae**: [Civitai](https://civitai.com/user/kitarz) ・ [Hugging Face](https://huggingface.co/NebulaeWis)
- [**narugo1992**](https://github.com/narugo1992) & [**deepghs**](https://huggingface.co/deepghs): open datasets, processing tools, and models
- [**Naifu**](https://github.com/Mikubill/naifu) trainer at [Mikubill](https://github.com/Mikubill)
# Community Contributors
**Evaluators & developers**: 二小姐, spawner, Rnglg2
**Other contributors**: 沉迷摸鱼, poi氵, ashan, 十分无奈, GHOSTLXH, wenaka, iiiiii, 年糕特工队, 恩匹希, 奶冻美宣集, mumu, yizyin, smile
# 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