Van-Gogh-diffusion
Maintainer: dallinmackay
277
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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 Van-Gogh-diffusion model is a fine-tuned Stable Diffusion model trained on screenshots from the film
Model inputs and outputs
The Van-Gogh-diffusion model takes text prompts as input and generates corresponding images in the Van Gogh style. The maintainer, dallinmackay, has found that using the token lvngvncnt at the beginning of prompts works best to capture the desired artistic look.
Inputs
- Text prompts describing the desired image, with the
lvngvncnttoken at the start
Outputs
- Images generated in the Van Gogh painting style based on the input prompt
Capabilities
The Van-Gogh-diffusion model is capable of generating a wide range of image types, from portraits and characters to landscapes and scenes, all with the distinct visual flair of Van Gogh's brush strokes and color palette. The model can produce highly detailed and realistic-looking outputs while maintaining the impressionistic quality of the source material.
What can I use it for?
This model could be useful for any creative projects or applications where you want to incorporate the iconic Van Gogh aesthetic, such as:
- Generating artwork and illustrations for books, games, or other media
- Creating unique social media content or digital art pieces
- Experimenting with AI-generated art in various styles and mediums
The open-source nature of the model also makes it suitable for both personal and commercial use, within the guidelines of the CreativeML OpenRAIL-M license.
Things to try
One interesting aspect of the Van-Gogh-diffusion model is its ability to handle a wide range of prompts and subject matter while maintaining the distinctive Van Gogh style. Try experimenting with different types of scenes, characters, and settings to see the diverse range of outputs the model can produce. You can also explore the impact of adjusting the sampling parameters, such as the number of steps and the CFG scale, to further refine the generated images.
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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