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--- |
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base_model: |
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- Alpha-VLLM/Lumina-Image-2.0 |
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license: other |
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license_name: fair-ai-public-license-1.0-sd |
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license_link: https://freedevproject.org/faipl-1.0-sd/ |
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--- |
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[中文版模型说明](https://huggingface.co/neta-art/Neta-Lumina/blob/main/README-ZH.md) |
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# Introduction |
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**Neta Lumina** is a high‑quality anime‑style image‑generation model developed by Neta.art Lab. |
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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. |
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## Key Features |
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- Optimized for diverse creative scenarios such as Furry, Guofeng (traditional‑Chinese aesthetics), pets, etc. |
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- Wide coverage of characters and styles, from popular to niche concepts. (Still support danbooru tags!) |
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- Accurate natural‑language understanding with excellent adherence to complex prompts. |
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- Native multilingual support, with Chinese, English, and Japanese recommended first. |
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## Model Versions |
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For models in alpha tests, requst access at https://huggingface.co/neta-art/NetaLumina_Alpha if you are interested. We will keep updating. |
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### neta-lumina-beta-0624-raw |
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- **Primary Goal**: General knowledge and anime‑style optimization |
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- **Data Set**: >13 million anime‑style images |
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- **>46,000** A100 Hours |
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- Higher upper limit, suitable for pro users. Check [**Neta Lumina Prompt Book**](https://nieta-art.feishu.cn/wiki/RVBgwvzBqiCvQ7kOMm1cM6NdnNc) for better results. |
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### neta-lumina-beta-0624-aes |
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- First beta release candidate |
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- **Primary Goal**: Enhanced aesthetics, pose accuracy, and scene detail |
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- **Data Set**: Hundreds of thousands of handpicked high‑quality anime images (fine‑tuned on an older version of raw model) |
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- User-friendly, suitable for most people. |
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<br> |
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# How to Use |
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[Try it at Hugging Face playground](https://huggingface.co/spaces/neta-art/NetaLumina_T2I_Playground) |
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## ComfyUI |
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Neta Lumina is built on the **Lumina2 Diffusion Transformer (DiT)** framework, please follow these steps precisely. |
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### Environment Requirements |
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Currently Neta Lumina runs only on ComfyUI: |
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- Latest ComfyUI installation |
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- ≥ 8 GB VRAM |
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### Downloads & Installation |
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**Original (component) release** |
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1. **Neta Lumina-Beta** |
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- Download link: https://huggingface.co/neta-art/Neta-Lumina/blob/main/neta-lumina-beta-0624.pth |
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- Save path: `ComfyUI/models/unet/` |
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2. **Text Encoder (Gemma-2B)** |
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- Download link:https://huggingface.co/neta-art/Neta-Lumina/resolve/main/gemma_2_2b_fp16.safetensors |
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- Save path: `ComfyUI/models/text_encoders/` |
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3. **VAE Model (16-Channel FLUX VAE)** |
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- Download link: https://huggingface.co/neta-art/Neta-Lumina/resolve/main/ae.safetensors |
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- Save path: `ComfyUI/models/vae/` |
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**Workflow**: load [`lumina_workflow.json`](https://huggingface.co/neta-art/NetaLumina_Alpha/blob/main/lumina_workflow.json) in ComfyUI. |
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- `UNETLoader` – loads the `.pth` |
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- `VAELoader` – loads `ae.safetensors` |
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- `CLIPLoader` – loads `gemma_2_2b_fp16.safetensors` |
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- `Text Encoder` – connects positive /negative prompts to K Sampler |
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**Simple merged release** |
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Download [`neta-lumina-beta-0624-all-in-one.safetensors`](https://huggingface.co/neta-art/Neta-Lumina/tree/main), |
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`md5sum = dca54fef3c64e942c1a62a741c4f9d8a`, |
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you may use ComfyUI’s simple checkpoint loader workflow. |
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### Recommended Settings |
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- **Sampler**: `res_multistep` |
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- **Scheduler**: `linear_quadratic` |
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- **Steps**: 30 |
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- **CFG (guidance)**: 4 – 5.5 |
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- **EmptySD3LatentImage resolution**: 1024 × 1024, 768 × 1532, or 968 × 1322 |
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<br> |
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# Prompt Book |
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Detailed prompt guidelines: [**Neta Lumina Prompt Book**](https://nieta-art.feishu.cn/wiki/RVBgwvzBqiCvQ7kOMm1cM6NdnNc) |
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<br> |
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# Community |
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- Discord: https://discord.com/invite/TTTGccjbEa |
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- QQ group: 785779037 |
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<br> |
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# Roadmap |
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## Model |
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- Continous base‑model training to raise reasoning capability. |
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- Aesthetic‑dataset iteration to improve anatomy, background richness, and overall appealness. |
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- Smarter, more versatile tagging tools to lower the creative barrier. |
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## Ecosystem |
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- LoRA training tutorials and components |
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- Experienced users may already fine‑tune via Lumina‑Image‑2.0’s open code. |
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- 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) |
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<br> |
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# License & Disclaimer |
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- Neta Lumina is released under the [**Fair AI Public License 1.0‑SD**](https://freedevproject.org/faipl-1.0-sd/) |
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- Any modifications, merges, or derivative models must themselves be open‑sourced. |
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<br> |
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# Participants & Contributors |
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- Special thanks to the **Alpha‑VLLM** team for open‑sourcing **Lumina‑Image‑2.0** |
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- **Model development**: **Neta.art Lab (Civitai)** |
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- Core Trainer: **li_li** [Civitai](https://civitai.com/user/li_li) ・ [Hugging Face](https://huggingface.co/heziiiii) |
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<br> |
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- **Partners** |
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- **nebulae**: [Civitai](https://civitai.com/user/kitarz) ・ [Hugging Face](https://huggingface.co/NebulaeWis) |
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- [**narugo1992**](https://github.com/narugo1992) & [**deepghs**](https://huggingface.co/deepghs): open datasets, processing tools, and models |
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- [**Naifu**](https://github.com/Mikubill/naifu) trainer at [Mikubill](https://github.com/Mikubill) |
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<br> |
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# Community Contributors |
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**Evaluators & developers**: 二小姐, spawner, Rnglg2 |
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**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 |
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<br> |
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# Appendix & Resources |
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- **TeaCache**: https://github.com/spawner1145/CUI-Lumina2-TeaCache |
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- **Advanced samplers & TeaCache guide (by spawner)**: https://docs.qq.com/doc/DZEFKb1ZrZVZiUmxw?nlc=1 |
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- **Neta Lumina ComfyUI Manual (in Chinese)**: https://docs.qq.com/doc/DZEVQZFdtaERPdXVh |
