Ghibli-Diffusion
Maintainer: nitrosocke
607
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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
The Ghibli-Diffusion model is a fine-tuned Stable Diffusion model trained on images from modern anime feature films from Studio Ghibli. This model allows users to generate images in the distinct Ghibli art style by including the _ghibli style_ token in their prompts. The model is maintained by nitrosocke, who has also created similar fine-tuned models like Mo Di Diffusion and Arcane Diffusion.
Model inputs and outputs
The Ghibli-Diffusion model takes text prompts as input and generates high-quality, Ghibli-style images as output. The model can be used to create a variety of content, including character portraits, scenes, and landscapes.
Inputs
- Text Prompts: The model accepts text prompts that can include the
_ghibli style_token to indicate the desired art style.
Outputs
- Images: The model generates images in the Ghibli art style, with a focus on high detail and vibrant colors.
Capabilities
The Ghibli-Diffusion model is particularly adept at generating character portraits, cars, animals, and landscapes in the distinctive Ghibli visual style. The provided examples showcase the model's ability to capture the whimsical, hand-drawn aesthetic of Ghibli films.
What can I use it for?
The Ghibli-Diffusion model can be used to create a wide range of Ghibli-inspired content, from character designs and fan art to concept art for animation projects. The model's capabilities make it well-suited for creative applications in the animation, gaming, and digital art industries. Users can also experiment with combining the Ghibli style with other elements, such as modern settings or fantastical elements, to generate unique and imaginative images.
Things to try
One interesting aspect of the Ghibli-Diffusion model is its ability to generate images with a balance of realism and stylization. Users can try experimenting with different prompts and negative prompts to see how the model handles a variety of subjects and compositions. Additionally, users may want to explore how the model performs when combining the _ghibli style_ token with other artistic styles or genre-specific keywords.
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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