{
"engine": "kohya",
"unetLR": 0.0005,
"clipSkip": 1,
"loraType": "lora",
"keepTokens": 3,
"networkDim": 128,
"numRepeats": 7,
"resolution": 1024,
"lrScheduler": "cosine",
"minSnrGamma": 0,
"noiseOffset": 0.03,
"targetSteps": 9992,
"enableBucket": true,
"networkAlpha": 16,
"optimizerType": "Adafactor",
"textEncoderLR": 0.00005,
"maxTrainEpochs": 61,
"shuffleCaption": true,
"trainBatchSize": 5,
"flipAugmentation": true,
"lrSchedulerNumCycles": 3
}
will post images that use this... so please don't report my models, it just takes me some time to work it out.
there are more sample images this time around.
do check them out.
hope it is sufficient for your imaginations.
but seems there is a lot of confusion. so to help those people out:
very handsome malay burly beefy grandfather just1nhigh quality realistic photogeneral army militaryfull bodywalking shopping mallconfident fierce muscular thighsmiddle aged equinecock realistic photohairytry to use the above in short 2 -3 words sentences and you should be good.
Description
[additional_network_arguments]
unet_lr = 0.0005
text_encoder_lr = 5e-5
network_dim = 128
network_alpha = 16
network_module = "networks.lora"
larger file sizes are due to this, trying to store more info in each lora.
there are more sample images this time around.
do check them out.
hope it is sufficient for your imaginations.
but seems there is a lot of confusion. so to help those people out:
very handsome malay burly beefy grandfather just1nhigh quality realistic photogeneral army militaryfull bodywalking shopping mallconfident fierce muscular thighsmiddle aged equinecock realistic photohairytry to use the above in short 2 -3 words sentences and you should be good.
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