Double-Exposure-Embedding
Maintainer: joachimsallstrom
82
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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
Double-Exposure-Embedding is a custom image embedding trained by joachimsallstrom to apply a distinct "double exposure" visual effect when used with the Stable Diffusion 2.x text-to-image model. This embedding was trained on a dataset of layered 768px images of people with a variety of surroundings, allowing it to handle objects and animals well. It can be triggered in prompts by writing "dblx".
Similar models like stable-diffusion-2 and stable-diffusion-2-base provide the underlying Stable Diffusion 2.x model which can be used with various custom embeddings and tunings.
Model inputs and outputs
Inputs
- Text prompt: A text description of the desired image.
- Model embedding: The
dblxembedding file which applies the double exposure effect.
Outputs
- Generated image: A unique image created by the Stable Diffusion 2.x model based on the input text prompt and "dblx" embedding.
Capabilities
The Double-Exposure-Embedding model can generate diverse and visually striking images by applying a "double exposure" effect. This allows for creative and surreal image composition, combining elements like faces, objects, and environments in unexpected ways. The model performs well on a range of subject matter, from portraits to landscapes.
What can I use it for?
The Double-Exposure-Embedding model can be a powerful tool for creative projects and artistic expression. It can be used to generate unique album covers, book illustrations, concept art, and more. The distinct visual style it produces may also be useful for film, photography, and design applications that require an unconventional or dreamlike aesthetic.
Things to try
Experiment with different text prompts that play to the model's strengths, such as those describing people, nature, or fantastical elements. Try combining the "dblx" embedding with prompts for specific artistic styles or subjects to see how it transforms the generated images. The model's ability to blend disparate elements in a cohesive way opens up many possibilities for imaginative and evocative visual 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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