veryBadImageNegative - veryBadImageNegative_v13
Parole Chiave e Tag Correlati
Immagini in evidenza
Prompt Consigliati
(((masterpiece,best quality,official art,extremely detailed CG unity 8k wallpaper))),illustration,landscape,solo,1girl,bishojo,beautiful detailed eyes,Student uniforms
Prompt Negativi Consigliati
no prompt
Parametri Consigliati
samplers
steps
cfg
resolution
other models
Parametri Consigliati per Alta Risoluzione
upscaler
upscale
steps
denoising strength
Suggerimenti
When using veryBadImageNegative_v1.2, you can reduce some weights to obtain better flexibility. For example: (veryBadImageNegative_v1.2-6400:0.9)
The number of training steps (1600 ~ 6400) represents the number of steps in training. In theory, the more steps, the better the effect.
Sponsor del Creatore
v1.3
veryBadImageNegative_v1.3使用了新的训练图集,在AOM3和viewer-mix_v1.7中使用时表现较好。
veryBadImageNegative_v1.3 uses a new training atlas and performs well when used in AOM3 and viewer-mix_v1.7.
v1.2
veryBadImageNegative是使用viewer-mix_v1.3生成的特殊图集训练而来的负面嵌入。
所以veryBadImageNegative是viewer-mix_v1.3的专用负面嵌入。
或许在其他的扩散模型中也有不错的效果,但缺乏验证。
veryBadImageNegative is a negative embedding trained from the special atlas generated by viewer-mix_v1.3.
So veryBadImageNegative is the dedicated negative embedding of viewer-mix_v1.3.
It may also have a good effect in other diffusion models, but it lacks verification.
关于v1.2:
增强图像的质量,削弱了风格。
在使用v1.2版本时,可以降低一些权重以获得更好的灵活性。
例如:(veryBadImageNegative_v1.2-6400:0.9)
About v1.2:
Enhance image quality and weaken style.
When using v1.2, you can reduce some weights to obtain better flexibility.
For example: (veryBadImageNegative_v1.2-6400:0.9)
关于v1.0与v1.1的差异:
几乎没有差别,仅用于训练的图集在生成参数有一些微妙的差别。
关于1600~6400的差异:
数字代表了训练步数,理论上步数越多效果越好。
About the difference between v1.0 and v1.1:
There is almost no difference. The atlas used for training only has some subtle differences in the generation parameters.
About the difference between 1600 ~ 6400:
The number represents the number of training steps. In theory, the more steps, the better the effect.
Dettagli del Modello
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Modello base
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Hash del modello
Parole addestrate
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