
At the start I'm training only pixelated censoring. I would like to first fine tune dataset/training on understanding of the concept of censoring on any object/part of object with natural language.
Version comparison
-
ALPHA2 is much more flexible than ALPHA1
-
ALPHA2 is able to censore things that were not present in training dataset
-
ALPHA2 has a bit of issues with hands and quality (jpeg artifacts)
-
ALPHA2 is not censoring things that you do not specify as ALPHA1 did
-
ALPHA2 does not censore somtimes entire objects (mostly on bigger ones)
How to use
-
Trigger word censored
-
It works pretty well with natural language prompts (prompts that seemed to work for me are adding to the end "<something> is/are censored.", before the thing you wanna censore add "censored <something>", or add right behind "<somthing> that is /are censored")
-
Don't use this LoRA on clip or keep it on strength 1
Known issues
-
Jpeg artifacts in certain parts of the image
-
Hands are generated blurry or with wrong number of fingers
If you have some ideas for future types of censoring leave them in the comments.
Enjoy :)
描述:
训练词语: censored
名称: Flux_censored_alpha2.safetensors
大小 (KB): 18839
类型: Model
Pickle 扫描结果: Success
Pickle 扫描信息: No Pickle imports
病毒扫描结果: Success