Amy Smart

Sapphira is a nude model from Czech Republic.

Tanaye White

Tanaye is an American model who has been featured in the Sports Illustrated Swimsuit Issue.

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[ SDv1.4, 5e-03, 3000, v/s .199 ]

Raven Lyn

Raven is an American model who has been featured in the Sports Illustrated Swimsuit Issue.

Like my work? Buy me a beer!

[ SDv1.4, 5e-03, 3400, v/s .199 ]

Kamie Crawford

Kamie is an American television host and beauty pageant winner who has been featured in the Sports Illustrated Swimsuit Issue.

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[ SDv1.4, 5e-03, 2800, v/s .199 ]

Ellie Thumann

Ellie is an American YouTube personality who has been featured in the Sports Illustrated Swimsuit Issue.

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[ SDv1.4, 4e-03, 4800, v/s .199 ]

Cindy Kimberly

Cindy is a Dutch model who has been featured in the Sports Illustrated Swimsuit Issue.

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[ SDv1.4, 4e-03, 5200, v/s .202 ]

Adaora

Adaora is is a Nigerian-American model who has been featured in the Sports Illustrated Swimsuit Issue.

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[ SDv1.4, 4e-03, 3800, v/s .197 ]

介绍（中文）

基本信息

该页面为推荐用于 AnimeIllustDiffusion [1] 模型的所有文本嵌入（Embedding）。您可以从版本描述查看该文本嵌入的信息。

使用方法

您应该将下载到的负面文本嵌入文件置入您 stable diffusion 目录下的 embeddings 文件夹内。之后，您只需要在填写负面提示词处输入 badv4 即可。

评价参数

每个文本嵌入将从以下参数描述其特性：

正/负面文本嵌入：如果一个文本嵌入是正面文本嵌入，则您应该将它填写在正面提示词内发挥作用；反之，如果一个文本嵌入是负面文本嵌入，则您应该将它填写在负面提示词内发挥作用。

作用范围：一个文本嵌入主要影响的画面内容。

向量数：代表该文本嵌入占多少个词元。通常来说，您输入提示词中超过 75 个词元的部分会被忽略。因此，一个文本嵌入的向量数越少，您填入它之后剩余输入提示词的空间越多。

向量强度：一个词元向量值的大小。向量强度越高，效果越强。普通词元的向量强度为[-0.05,0.05]。不可将用括号增强提示词等效于增强向量强度。

Introduction (English)

Basic Information

This page contains all the text embeddings recommended for use with the AnimeIllustDiffusion model [1]. You can view information about the text embeddings from the version description.

Usage

You should place the downloaded negative text embedding file in the embeddings folder of your Stable Diffusion directory. After that, you only need to enter "badv4" in the field for negative prompts.

Evaluation Parameters

Each text embedding is described by the following parameters:

Positive/Negative Text Embedding: If a text embedding is a positive text embedding, it should be used in positive prompts. Conversely, if a text embedding is a negative text embedding, it should be used in negative prompts.

Scope: The main content of the image that a text embedding will primarily affect.

Vector Count: The number of token that the text embedding represents. Generally, the part of a prompt containing more than 75 tokens will be ignored. Therefore, the fewer the number of vectors in a text embedding, the more space you will have left to input the prompt.

Vector Strength: The magnitude of a token's vector. The higher the vector strength, the stronger the effect. The vector strength of token is usually between [-0.05, 0.05]. Using parentheses to enhance prompts is not equivalent to increasing vector strength.

引用 / References

[1] AnimeIllustDiffusion Model Webpage: https://civitai.com/models/16828/animeillustdiffusion

Textual Inversion of Bob Ross, pretty tough to get a good embedding due to lack of source images. Give it a try