bad-artist
Maintainer: nick-x-hacker
313
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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 bad-artist model is a textual-inversion embedding created by the Hugging Face user nick-x-hacker. This embedding can be used in the negative prompt of a Stable Diffusion model to generate unique and unconventional-looking images. Similar models like Counterfeit-V3.0 and Replicant-V2.0 also utilize negative prompts and embeddings to create distinctive artwork, each with their own aesthetic.
Model inputs and outputs
The bad-artist model takes a standard image generation prompt, like "solo", and applies a negative embedding of "sketch by bad-artist" to produce images with a unique, hand-drawn style. The model was trained on an Anything-v3-based model for 15,000 steps using only 2 tokens per embedding.
Inputs
- Standard image generation prompts, such as "solo"
- Negative prompt including "sketch by bad-artist"
Outputs
- Images with a unique, unconventional hand-drawn style
Capabilities
The bad-artist model can generate images with a distinct sketch-like aesthetic, using only a 2-token negative embedding. This allows for concise prompts that produce visually interesting and unexpected results. The model's capabilities contrast with more generic or anime-style negative embeddings, offering a unique artistic perspective.
What can I use it for?
The bad-artist model could be used to create quirky, one-of-a-kind illustrations or concept art with an unconventional style. Pairing it with other prompts or models like Counterfeit-V3.0 and Replicant-V2.0 could lead to even more unique and unexpected artistic outputs.
Things to try
Experiment with using the bad-artist negative embedding in combination with different positive prompts to see the range of styles it can produce. Try adding modifiers like "by [artist name]" to the negative prompt to see how the model blends different artistic influences. The concise nature of the embedding also makes it well-suited for rapid iteration and exploration of different creative directions.
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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