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.
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This version was fine-tuned on Anima Base v1.0 using a variety of datasets, including Danbooru, e621, Gelbooru, and Konachan. The training approach differs from the original model in several ways. Most notably, I did not include quality score tags in the training data.
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I also experimented with multiple captioning styles, ranging from traditional tag-based annotations to different forms of natural language descriptions, similar to the approach I used for Netayume Lumina.
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Note:
- This release has two versions, each trained using different methods. For the v1.0 Demo, I experimented with a mixed training approach, but it was difficult to control. The v1.0 Final is different from the v1.0 Demo, so please do not compare them directly, even though they were trained on the same dataset. I created both versions to test different ideas for training diffusion models. Since Anima is relatively small, it gives me the flexibility to experiment with various training methods and see what works best.
- Unlike Netayume, I did not use my full dataset for training. The complete dataset contains around 25 million images, and if I were to use the entire dataset, I would train a model from scratch rather than fine-tune an existing one. Additionally, I have been quite busy recently, so I have not been able to test this model as extensively as I would have liked. Moreover, this model does not have any default style :L.
- V1.0 Final has a watermark embedded in the model. However, this does not affect the results of generated images. Moreover, v1.0 currently supports chain-of-thought prompting, as demonstrated in my example images.
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If you encounter any issues or have any feedback, please feel free to share them with me.
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CFG: 4–7
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Sampling Steps: 25-40
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Sampler: Euler a (with scheduler: normal)
