Pony version <a rel="ugc" href="https://civarchive.com/models/685671/milf-apricota-pony">HERE.</a>This lora is trained on images produced from 2 different datasets, the first one combines images of 'Nicki Valentina Rose' and various bimbo images on the web. While the second is a merge of a 90s brunette pornstars with models from the black tape project.Version 1 was trained using civit ai lora trainerVersion 2 was trained using google colabthe two versions trained on the same datasets, the intention was to make version 1 easier on weak rigs, while let version 2 be as normal as other loras for flux.<hr />Trigger word: Apricota (using 'photo of apricota' helps). additional triggers: bimbo, massive breasts.Best between 0.7 ~ 1Training Config: for version 1<pre><code>unetLR = 0.0005
clipSkip = 1
networkDim = 2
numRepeats = 20
resolution = 1024
lrScheduler = cosine
minSnrGamma = 5
noiseOffset = 0.1
targetSteps = 4800
enableBucket = true
networkAlpha = 16
optimizerType = Prodigy
textEncoderLR = 0
maxTrainEpochs = 8
shuffleCaption = false
trainBatchSize = 1
lrSchedulerNumCycles = 3</code></pre><hr />Training Config: for version 2<pre><code>precision to save = float16
Training steps = 4000
Epochs = 16
caption dropout rate = 0.05
optimizer = adamw8bit
Learning rate = 4e-4
Batch size = 1
use ema = True
ema decay = 0.99
linear = 16
linear alpha = 16</code></pre><hr />one of the example images is not using the natural language prompting, and it seems to work.I tested this lora with<a target="_blank" rel="ugc" href="https://civarchive.com/models/638187/flux1-dev-v1v2-flux1-schnell-bnb-nf4"> flux1DevV1V2Flux1_flux1DevBNBNF4V2 and flux1DevV1V2Flux1_flux1SchnellBNBNF4</a>I used Forge Ui while testing and it worked fine with both.--if you are having issues making loras works locally in forge ui (with flux nf4)check this <a target="_blank" rel="ugc" href="https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1038">&gt;link&lt;</a>&gt;BUZZ for cool images&lt;Have fun with Apricota

Milf Apricota [Flux]

Version 2 Trained on Colab. same datasets as Version 1.Bigger in size and produce slightly better resaults.

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