VirtualAddressExtension
/

Neta-Lumina-v1.0-diffusers

@@ -1,198 +1,139 @@
 ---
 library_name: diffusers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: diffusers
+license: apache-2.0
+base_model:
+- neta-art/Neta-Lumina
+tags:
+- diffusers,
+- text-to-image
 ---
+# Neta Lumina v1.0 for diffusers library
+[**Neta Lumina Tech Report**](https://neta.art/blog/neta_lumina/)
+## 📽️ Flash Preview
+<video controls autoplay loop muted playsinline style="max-width:100%; border-radius:8px;">
+  <source src="https://pages-r2.neta.art/Neta_Lumina_Flash_PV.webm" type="video/webm" />
+  Your browser does not support the video tag.
+</video>
+# 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**](https://nieta-art.feishu.cn/wiki/RY3GwpT59icIQlkWXEfcCqIMnQd) 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.
+<br>
+# How  to  Use
+[Try it at Hugging Face playground](https://huggingface.co/spaces/neta-art/NetaLumina_T2I_Playground)
+## Or use it with diffusers:
+```python
+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**](https://neta.art/blog/neta_lumina_prompt_book/)
+<br>
+# Community
+- Discord: https://discord.com/invite/TTTGccjbEa
+- QQ group: 1039442542
+<br>
+# 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)
+<br>
+# License & Disclaimer
+- Neta Lumina is released under [**Apache License 2.0**](https://www.apache.org/licenses/LICENSE-2.0)
+<br>
+# 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)
+<br>
+- **Partners**
+  - **nebulae**: [Civitai](https://civitai.com/user/kitarz) ・ [Hugging Face](https://huggingface.co/NebulaeWis)
+  - **生姜**: [Hugging Face](https://huggingface.co/ssj0021)
+  - **孙一**
+- [**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)
+<br>
+# Community Contributors
+- **Evaluators & developers**: [二小姐](https://huggingface.co/Second222), [spawner](https://github.com/spawner1145), [Rnglg2](https://civitai.com/user/Rnglg2)
+- **Other contributors**: [沉迷摸鱼](https://www.pixiv.net/users/22433944), [poi](https://x.com/poi______1), AshenWitch, [十分无奈](https://www.pixiv.net/users/15750592), [GHOSTLX](https://civitai.com/user/ghostlxh), [wenaka](https://civitai.com/user/Wenaka_), [iiiiii](https://civitai.com/user/Blueberries_i), [年糕特工队](https://x.com/gaonian2331), [恩匹希](https://civitai.com/user/NPCde), 奶冻, [mumu](https://civitai.com/user/mumu520), [yizyin](https://civitai.com/user/yizyin), smile, Yang, 古神, 灵之药, [LyloGummy](https://civitai.com/user/LyloGummy), 雪时
+<br>
+# 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