hitokomoru-diffusion
Maintainer: Linaqruf
78
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
hitokomoru-diffusion is a latent diffusion model that has been trained on Japanese Artist artwork, /Hitokomoru. The current model has been fine-tuned with a learning rate of 2.0e-6 for 20000 training steps/80 Epochs on 255 images collected from Danbooru. The model is trained using NovelAI Aspect Ratio Bucketing Tool so that it can be trained at non-square resolutions. Like other anime-style Stable Diffusion models, it also supports Danbooru tags to generate images.
There are 4 variations of this model available, trained for different numbers of steps ranging from 5,000 to 20,000.
Similar models include the hitokomoru-diffusion-v2 model, which is a continuation of this model fine-tuned from Anything V3.0, and the cool-japan-diffusion-2-1-0 model, which is a Stable Diffusion v2 model focused on Japanese art.
Model inputs and outputs
Inputs
- Text prompt: A text description of the desired image to generate, which can include Danbooru tags like "1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden".
Outputs
- Generated image: An image generated based on the input text prompt.
Capabilities
The hitokomoru-diffusion model is able to generate high-quality anime-style artwork with a focus on Japanese artistic styles. The model is particularly skilled at rendering details like hair, eyes, and natural environments. Example images showcase the model's ability to generate a variety of characters and scenes, from portraits to full-body illustrations.
What can I use it for?
You can use the hitokomoru-diffusion model to generate anime-inspired artwork for a variety of purposes, such as illustrations, character designs, or concept art. The model's ability to work with Danbooru tags makes it a flexible tool for creating images based on specific visual styles or themes.
Some potential use cases include:
- Generating artwork for visual novels, manga, or anime-inspired media
- Creating character designs or concept art for games or other creative projects
- Experimenting with different artistic styles and aesthetics within the anime genre
Things to try
One interesting aspect of the hitokomoru-diffusion model is its support for training at non-square resolutions using the NovelAI Aspect Ratio Bucketing Tool. This allows the model to generate images with a wider range of aspect ratios, which can be useful for creating artwork intended for specific formats or platforms.
Additionally, the model's ability to work with Danbooru tags provides opportunities for experimentation and fine-tuning. You could try incorporating different tags or tag combinations to see how they influence the generated output, or explore the model's capabilities for generating more complex scenes and compositions.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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