I. Introduction
AnimaYume is a text-to-image model fine-tuned from Anima, a high-quality anime-style image generation model developed by CircleStone Labs. It builds upon Cosmos 2, a model developed by NVIDIA’s research team.
II. Information
For version 0.1:
This model is a preview version fine-tuned from the Anima base model using a custom dataset. Training was conducted across multiple resolutions ranging from 768 to 1280 pixels, with a primary focus around 1024. The goal of this release is to improve stability and minimize unwanted artifacts when producing high-resolution images.
Notes: All the example images at this version were generated at the resolution 1024x1536 or 1536x1024
For version 0.2:
This model is a continuation of AnimeYume v0.1. In this version, I improved the quality of my dataset and used several techniques to prevent oversaturation and low-quality outputs. Based on my testing phase, I observed that the prompt coherence is better than v0.1, and the model remains very stable when generating images at a resolution of 1536.
Note: I am still waiting for the final version of Anima and testing some methods to make my training process faster. I know the license might make the model less popular, but I only care about whether the model is good or not. I’m aware that many others use better licenses, but I’m too lazy to spend a bunch of money training a model from scratch.
III. File Information
This file contains only the diffusion model and does not include a VAE or text encoder. To use it properly, you will need to download those components from the link here
IV. Notes & Feedback
This is an experimental fine-tuned release, and I am waiting for the final version release to tune it :D
Your feedback, suggestions, and creative prompt ideas are always welcome, every contribution helps make this model even better!
V. Acknowledgments
Big thanks to narugo1992 for the dataset contributions.
Credit to Circlestone Labs and Nvidia for the fantastic base model architecture.
If you'd like to support my work, you can do so through Ko-fi!