badquality
Maintainer: p1atdev
47
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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 badquality model is a negative prompt embedding developed by p1atdev for use with the Waifu Diffusion 1.5 beta. This embedding is designed to help users avoid generating low-quality, undesirable outputs when using the Waifu Diffusion model. Similar models like plat-diffusion, pvc, bad-artist, and Replicant-V2.0 also provide ways to control and refine the output of text-to-image models.
Model inputs and outputs
The badquality model is a negative prompt embedding, meaning it is used in the negative prompt to guide the model away from generating certain types of outputs. The input is a text prompt that includes the badquality token, which signals to the model to avoid generating low-quality, undesirable imagery.
Inputs
- Positive prompt: The main text prompt describing the desired output image
- Negative prompt: The text prompt that includes the
badqualitytoken to avoid generating low-quality outputs
Outputs
- Image: The generated image based on the provided prompts
Capabilities
The badquality model is effective at preventing the generation of low-quality, undesirable images when used in the negative prompt. By including badquality in the prompt, users can steer the model away from outputs that may be blurry, low-resolution, or have other quality issues.
What can I use it for?
The badquality model can be useful for users of the Waifu Diffusion text-to-image model who want greater control over the quality of their generated outputs. By incorporating the badquality token into their prompts, users can improve the consistency and visual fidelity of the images produced.
Things to try
One interesting aspect of the badquality model is that it can be used in conjunction with other negative prompt tokens to further refine the output. For example, users could combine badquality with other terms like "lowres" or "bad anatomy" to target specific quality and stylistic issues. Experimenting with different negative prompt combinations can help users find the right balance for their desired output.
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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