This is for all the freaks out there. This lora can work as a detailer lora or a main image lora. As a main image lora, I recommend lowering the strength to about 0.7 to prevent style bleed. When used in the detailer you can bump that weight up to 1 or higher. The trigger words are listed on the right, be sure to mix and match and add parenthesis if needed. Surprisingly, the monster equine penis dataset I have is by far the smallest of the 4 types I put together. The result is less variety in the types of peens it can produce, but it still works well imo. There are 2 ways this lora can be used and both methods were used to generate the sample pics:<ol><li>Use as a normal lora. Works but can change the style and composition of your image. Use a weight of 0.7 or lower to reduce that. You can always add a higher weight to the lora in the detailer later to bump it up in the details.</li><li>Use only as a detailer. Great way to boost the peens visuals with 0 affect on overall image style. I personally recommend this approach but I know its not available to everyone.</li></ol> For those curious on how to do method 2, here's a quick guide.<ol><li>Generate your base image the same way you usually would up until applying detailers. Leave out the lora or include it with a low weight, like 0.7.</li><li>Add trigger tags like "m0nster equ1ne pp, nubbed penis, spiked penis, penis, ribbed penis" if you want a specific look.</li><li>Plug this lora into either your adetailer or fdetailer.</li><li>Adjust the weight of the lora and the denoise of the detailer as you want. In my testing, a weight of 1 on the lora and a denoise of 0.40 is the sweet spot. The higher the denoise the higher the risk/reward.</li><li>If you're struggling, check the quick example workflows I have attached to the sample pics.</li></ol>

Monster Equine Penis

Trained on the below params. 113 images: { "engine": "kohya", "unetLR": 0.0005, "clipSkip": 2, "loraType": "lora", "keepTokens": 1, "networkDim": 16, "numRepeats": 7, "resolution": 1024, "lrScheduler": "cosine", "minSnrGamma": 5, "noiseOffset": 0.1, "targetSteps": 3955, "enableBucket": true, "networkAlpha": 8, "optimizerType": "Prodigy", "textEncoderLR": 0.00005, "maxTrainEpochs": 20, "shuffleCaption": true, "trainBatchSize": 4, "flipAugmentation": true, "lrSchedulerNumCycles": 3}

Files with SHA256: b951154517cfe8e965f72012f51946ef318d29759071f63f9e25e832d26929ea