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Open-weight model · Text to image

NetaYume-Lumina-Image-2.0

by Nguyễn Vũ Dương duongve/NetaYume-Lumina-Image-2.0

I. Introduction NetaYume Lumina is a text-to-image model fine-tuned from Neta Lumina, a high-quality anime-style image generation model developed by Neta.art Lab.

Parameters
Context
Weights110.9 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads140.1k

Model Card

By Nguyễn Vũ Dương, published under apache-2.0, revision 69c29c441119.

I. Introduction NetaYume Lumina is a text-to-image model fine-tuned from Neta Lumina, a high-quality anime-style image generation model developed by Neta.art Lab. It builds upon Lumina-Image-2.0, an open-source base model released by the Alpha-VLLM team at Shanghai AI Laboratory. This model was trained with the goal of not only generating realistic human images but also producing high-quality anime-style images. Despite being fine-tuned on a specific dataset, it retains a significant amount of knowledge from the base model. The file NetaYumeLuminav2allinone.safetensors is an all-in-one file that contains the necessary weights for the VAE, text encoder, and image backbone to be used with…

Read Nguyễn Vũ Dương's full model card

NetaYume Lumina Image v2.0


I. Introduction

NetaYume Lumina is a text-to-image model fine-tuned from Neta Lumina, a high-quality anime-style image generation model developed by Neta.art Lab. It builds upon Lumina-Image-2.0, an open-source base model released by the Alpha-VLLM team at Shanghai AI Laboratory.

This model was trained with the goal of not only generating realistic human images but also producing high-quality anime-style images. Despite being fine-tuned on a specific dataset, it retains a significant amount of knowledge from the base model.

Key Features: - High-Quality Anime Generation: Generates detailed anime-style images with sharp outlines, vibrant colors, and smooth shading. - Improved Character Understanding: Better captures characters, especially those from the Danbooru dataset, resulting in more coherent and accurate character representations. - Enhanced Fine Details: Accurately generates accessories, clothing textures, hairstyles, and background elements with greater clarity.

The file NetaYume_Lumina_v2_all_in_one.safetensors is an all-in-one file that contains the necessary weights for the VAE, text encoder, and image backbone to be used with ComfyUI.


II. Model Components & Training Details - Text Encoder: Pre-trained Gemma-2-2b - Variational Autoencoder: Pre-trained Flux.1 dev's VAE - Image Backbone: Fine-tune NetaLumina's Image Backbone


III. Suggestion

System Prompt: This help you generate your desired images more easily by understanding and aligning with your prompts.

For anime-style images using Danbooru tags:

 You are an assistant designed to generate anime images based on textual prompts.

 You are an assistant designed to generate high-quality images based on user prompts and  danbooru tags.

Recommended Settings - CFG: 4–7 - Sampling Steps: 40-50 - Sampler: - Euler a (with scheduler: normal) - res_multistep (with scheduler: linear_quadratic)


IV. Acknowledgments - narugo1992 – for the invaluable Danbooru dataset - Alpha-VLLM - for creating the a wonderful model! - Neta.art and his team – for openly sharing awesome model.

Identity and Version

Repository
duongve/NetaYume-Lumina-Image-2.0
Publisher
Nguyễn Vũ Dương
Task
Text to image
Modality
Image
Library
diffusion-single-file
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
69c29c441119d43405d8373c654b16827d3cf46e
First published
2025-08-06
Last updated
2025-12-09

Files and Weights

21 files, 110.9 GB in total. The weights are 16 files totalling 110.9 GB in pth, safetensors.

Weights16 files · 110.9 GB
Configuration2 files · 19.6 KB
Documentation1 file · 2.8 KB
Other1 file · 6.5 MB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
NetaYume_Lumina_v2_all_in_one.safetensorsWeights10.6 GB 9fede732bfd6
NetaYume_v2_plus_all_in_one.safetensorsWeights10.6 GB 0327f877d8cc
NetaYume_v3_all_in_one.safetensorsWeights10.6 GB 0811ee874254
NetaYume_v4_all_in_one.safetensorsWeights10.6 GB e2b277eedf4f
NetaYumev35_pretrained_all_in_one.safetensorsWeights10.6 GB 4125cb490996
Text_Encoder/gemma_2_2b_fp16.safetensorsWeights5.2 GB 29761442862f
Trained_weights_and_config/v2/LuminaYume_v2_consolidated.00-of-01.pthWeights10.4 GB 3471b86739b5
Trained_weights_and_config/v2/model_args.pthWeights1.7 KB 8ef126675409
Trained_weights_and_config/v2_plus/consolidated.00-of-01.pthWeights10.4 GB 9deba44d4deb
Unet/v1/NetaYume_Lumina_v1_unet.safetensorsWeights5.2 GB 6e1d91f8f635
Unet/v2/NetaYume_Lumina_v2_unet.safetensorsWeights5.2 GB 11726455f850
Unet/v2_plus/NetaYumev2plus_unet.safetensorsWeights5.2 GB ac6be726d492
Unet/v3/NetaYumev3_unet.safetensorsWeights5.2 GB 2dadef4810bc
Unet/v3_5/pretrained/NetaYumev35_pretrained_unet.safetensorsWeights5.2 GB 78af3f428120
Unet/v4/NetaYumev4_unet.safetensorsWeights5.2 GB cc79f7e52a39
Vae/vae.safetensorsWeights335.3 MB afc8e28272cd
Lumina_image_v2_tensorart_workflow.jsonConfiguration8.1 KB
Script_Lora_Convert/Convert_lora_format_between_comfyui_diffusers.pyConfiguration11.5 KB
README.mdDocumentation2.8 KB
Example/Demo_v2.pngOther6.5 MB 6653a24c83d4
.gitattributesRepository1.6 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
110.9 GB
Download from Nguyễn Vũ Dương

Released by Nguyễn Vũ Dương through its official repository on Hugging Face. Read the license.

Built From

  • Derived from Alpha-VLLM/Lumina-Image-2.0

Memory Requirements

PrecisionWeights in memory
As published110.9 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About NetaYume-Lumina-Image-2.0

Can I use NetaYume-Lumina-Image-2.0 commercially?

Yes. NetaYume-Lumina-Image-2.0 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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