NSFW Adv. is a LoKr trained on Qwen-image FP8, focused on refining and adding details to the human body. It was trained on a curated dataset of over 3000 images. I had to scrap it and start over from scratch more than 10 times, with this final version being trained for over 100,000 steps.Even with all that effort, it's still got plenty of flaws. But after grinding on it for over two months, I had to push it out. The whole process was honestly super frustrating—tweaking, testing, culling, and repeating the process with a 10,000+ image dataset... it was just so damn hard. I seriously thought I might never finish it. But anyway, it's finally out. Hope you guys have fun with it.IMPORTANT USAGE TIPS: First, you'll need to install this plugin for your ComfyUI: <a target="_blank" rel="ugc" href="https://github.com/ClownsharkBatwing/RES4LYF">https://github.com/ClownsharkBatwing/RES4LYF</a> Once it's installed, you'll get a bunch of new, high-quality samplers and schedulers in your ComfyUI. I recommend using Sampler: res_2s and Scheduler: bong_tangent.I also recommend using it with this distilled LoRA for faster generation: <a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors?download=true">https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-8steps-V2.0-bf16.safetensors?download=true</a>All demo images were generated using: Steps: 10 CFG: 1 Sampler: res_2s Scheduler: bong_tangentNSFW Adv lora strength 1.0Qwen-Image-Lightning-8steps lora strength 0.4~0.5PS: If you plan on merging this, please give credit or link back to this page. ThanksPS2: This LoRA training session was done with <a target="_blank" rel="ugc" href="https://github.com/ostris/ai-toolkit">https://github.com/ostris/ai-toolkit</a>, but I incorporated the new sampling methods for timesteps from sdbds (<a target="_blank" rel="ugc" href="https://github.com/kohya-ss/musubi-tuner/pull/407">https://github.com/kohya-ss/musubi-tuner/pull/407</a>).After several experiments, I found this method to be significantly effective for this training task, as it notably accelerated convergence. A big shoutout to them for their excellent contribution.

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