<h1 id="this-lora-is-trained-mainly-on-front-images">this lora is trained mainly on front images</h1>trigger: chastity cage, padlock, testiclesextra: heart-shaped lockcolors: pink chastity cage, pink padlock, etc.<h2 id="v1.7-(more-real)">v1.7 (more real)</h2>trained on same dataset as v1.5 but with adjustments that should have improved quality. doesn't use anime upscaler so images will look more "realistic"<h2 id="v1.5-(more-anime)">v1.5 (more anime)</h2>not as good as v1.7 but doesn't make stuff realistic

Chastity Cage (Padlocked Colored Tube)

trained on 111 images incl 99 photos tagged "photorealistic" some colored drawings and 5 greyscale manga. all images were tagged with "chastity cage, padlock" and then additional features like "testicles" (102) and "pink chastity cage" (82) and "heart-shaped lock" (8). cropped images to better learn detail, 62 crotch focus and 20 "chastity focus". all images were 4x upscaled up to 1024 with anime R ESRGAN Anime6B to get rid of jpeg artifacts. some were pretty low res tbh and that might have messed with details like on the padlockthis version was mainly trained on front images because that's where the padlock is duh. there's some data from behind/side/on back but it's not very good at thosei had some pics of dildos/anal sex to it can gen penises normally but it's going to add padlocks to chokers.i used close ups of photos to make the ai learn the details then drawings of characters wearing chastity with their heads visible so the ai doesn't get stuck rendering only the cage no matter what you prompt and to stop making the base style realistic/smooth. worked ok igi trained both a 32/16 dim/alpha and an 8/4 with the same amount of steps but the 8/4 ended up too weak. this is the 32/16. maybe if i had made it 8/16 would have turned better?<pre><code>{
 "engine": "kohya",
 "unetLR": 0.0005,
 "clipSkip": 1,
 "loraType": "lora",
 "keepTokens": 1,
 "networkDim": 32,
 "numRepeats": 2,
 "resolution": 1024,
 "lrScheduler": "cosine_with_restarts",
 "minSnrGamma": 5,
 "noiseOffset": 0,
 "targetSteps": 1110,
 "enableBucket": true,
 "networkAlpha": 16,
 "optimizerType": "Adafactor",
 "textEncoderLR": 0.00005,
 "maxTrainEpochs": 20,
 "shuffleCaption": true,
 "trainBatchSize": 4,
 "flipAugmentation": true,
 "lrSchedulerNumCycles": 3
}</code></pre>

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