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.
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.
II. Information
For version 1.0:
This model was fine-tuned from the NetaLumina model, version
neta-lumina-beta-0624-raw, using a custom dataset consisting of approximately 10 million images. Training was conducted over a period of 3 weeks on 8× NVIDIA B200 GPUs.
For version 2.0:
This version has 2 versions:
Version 2.0:
I switched the base model to Neta Lumina v1 and trained this model on my custom dataset, which consists of images sourced from both e621 and Danbooru. The dataset is annotated with a mix of languages: 30% of the images are labeled in Japanese, 30% in Chinese (50% using Danbooru-style tags and 50% in natural language), and the remaining 40% in natural English descriptions.
For annotations, I used ChatGPT along with other models capable of prompt refinement to improve tag quality. Additionally, instead of training at a fixed resolution of 1024, I modified the code to support multiscale training, dynamically resizing images between 768 and 1536 during training.
Notes: Currently, I've only evaluated this model using benchmark tests, so its full capabilities are still uncertain. However, based on my initial testing, the model performs quite well when generating images at a resolution of 1312x2048 (as shown in the sample images I provided).
Moreover, this version the model generates images with the size up to 2048x2048 based on my testing.
Version 2.0 plus:
This model is fine-tuned from version 2.0, which had been trained on a dataset of higher-quality images. In this dataset, each image is annotated with both natural language descriptions and Danbooru-style tags.
The training procedure follows the same overall design as version 2, but is divided into three stages.
In the first two stages, the top 10 layers are frozen, and training is performed separately on the Danbooru-labeled subset and the natural language-labeled subset.
In the final stage, all layers are unfrozen and optimized jointly on the full dataset, which incorporates both Danbooru and natural language annotations.
This version reduces the issue of generated images exhibiting an artificial or 'AI-like' appearance, while also improving spatial understanding. For instance, the model is able to generate images in which a character is positioned on the left or right side of the images according to the prompt (as illustrated in the example). In addition, it provides modest improvements in rendering artist-specific styles.
You can find gguf quantization at here: https://huggingface.co/Immac/NetaYume-Lumina-Image-2.0-GGUF
III. Model Components:
Text Encoder: Pretrained Gemma-2-2B
VAE: From Flux.1 dev's VAE
Image Backbone: Fine-tuned version of NetaLumina's backbone
IV. File Information
This all-in-one file includes weights for VAE, text encoder, and image backbone. Fully compatible with ComfyUI and other systems supporting custom pipelines.
If you only want to download the image backbone, feel free to visit my Hugging Face page, it includes the separated files along with the
.pthfiles in case you want to use them for fine-tuning.
V. Suggestion Settings
For more details and to achieve better results, please refer to the Neta Lumina Prompt Book.
VI. Notes & Feedback
This is an early experimental fine-tuned release, and I’m actively working on improving it in future versions.
Your feedback, suggestions, and creative prompt ideas are always welcome — every contribution helps make this model even better!
VII. How to Run the Model on Another Platform
You can use it through the tensor.art platform. Here is the model link: https://tensor.art/models/898410886899707191
However, to run the model in an optimized way, I recommend using Comfyflow from tensor.art (because its default runner lacks configuration, which makes the model run suboptimally). Here is an example flow you can use on the platform: https://huggingface.co/duongve/NetaYume-Lumina-Image-2.0/blob/main/Lumina_image_v2_tensorart_workflow.json
VIII. Acknowledgments
Big thanks to narugo1992 for the dataset contributions.
Credit to Alpha-VLLM and Neta.art Lab for the fantastic base model architecture.
If you'd like to support my work, you can do so through Ko-fi!