Pyro's SD Models

Pyro's SDXL models - Blowjob edition Like what you see? Help me create more models by supporting my work. Your contribution can make a real difference. 👌 ❤️❤️ Support Here ❤️❤️ Want more details? Click on 'Show more,' and check out my images for prompt ideas! Have fun with this model! 💦 Hi there! You probably know me from my SD1.5 models, or you don't, which is fine too... Anyway, I'm back with an SDXL version of my SD1.5 Blowjob model. It's pretty alpha since I'm still experimenting with training parameters and such, but for a first try, I think the results are pretty good! Depending on the prompt every 4-5 images you get something usable. All example pictures are straight out of the model. Did like 30 generations for those pictures. I, of course, want to improve on this model and am also planning models focusing on non-sex nudity, a POV riding model, and, of course, at the end, a general purpose NSFW model. But to be frank, this shit's expensive and time-consuming, and therefore your support would be highly appreciated. Also, if you have a collection of photos you think would make a good dataset that I can use for further training, feel free to reach out FAQ How to use? Just write in natural language what you want to see, like "a woman sucking a cock". No Booru tags yet. Take a look at this rentry to get an idea of what prompts the model was trained on, and use similar expressions. Captions were generated with BLIP, and it somehow likes "big dille" whatever this is. https://rentry.org/pyros-sdxl-bj Why a fine-tune and not a LoRa? So people have a base to merge different kinds of base models, and training the whole model leads to better results. Merge it with one of the nudity models for example as long as I'm not done with my general purpose NSFW model ;) Where can I find your SD 1.5 stuff? They were made before Civitai was really a thing so they're hosted on mega: https://rentry.org/pyros-sd-model https://rentry.org/pyros-pov-model Some technical stuff: Training framework: Kohya's on RunPod A100 Dataset: 3388 images Batch size: 4 Steps: 22022 / 26 epochs Learning Rate: 5e-6