Z-Image TurboTrained with over 650 images and 30,000 steps, this can produce decent results, but also monsters. Workflows are embedded for what I used for the samples. I used it with my Speedo Tan Line LoRA and set them to about 0.6 each otherwise I got trash. I used sa_solver/beta, 12 steps seed_v3/beta is my current choice but that changes like every weekTrigger word: pen15TLDR:These ZIT LoRA were my first attempt at anything other than character LoRAs, so I don't have detailed tests as to exactly what worked, but here's the rundown:I have started to do captioning through the QwenVL node in ComfyUI using the NSFW model, Qwen3-VL-4B-Thinking-abliterated (NSFW), using a workflow that batch processes a folder of images to resize and caption themMany of these images for this one were captioned with Python code I modified to batch process image files using joy-caption-alpha-two. It did not do a good job at differentiating between flaccid and erect. I have another more focused dataset that I resized and cropped all the images to 512 squares and has all erect penises (coming soon), but this one surprisingly gave equal or better results for ZIT at least.For my latest character lora, I followed the advice from this <a target="_blank" rel="ugc" href="https://civarchive.com/articles/11942/training-a-wan-or-hunyuan-lora-the-right-way">article</a> with excellent results, and gave QwenVL these instructions:Caption EVERYTHING you see except for the man and his hair or body type. That means outfit, backgrounds, lighting, camera angles, skin details, water droplets, etc. must all go into the caption. Refer to him as BrockI used AI Toolkit and its adapter to train this on my 5090:ostris/zimage_turbo_training_adapter/zimage_turbo_training_adapter_v2.safetensors

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