This LoRA is trained on various strappy lingerie and bondage-inspired outfits, including leather and latex harness sets with metal rings, buckles, and chains, cage bras, corsets, and garter belts with attached thigh straps. It also covers mesh and lace bodysuits with strap patterns, halter and choker designs, cupless bras, and harnesses that wrap around the bust, waist, hips, and thighs. Materials include leather, latex, satin, lace, mesh, and elastic straps in colors such as black, red, white, and metallic gold. Accessories like chokers, cuffs, and chain details are common, and the style ranges from fetish-inspired harness lingerie to elegant strappy fashion sets.
The LoRA was trained on the default flux1-dev model.
Dataset and training data:
136 images
9 steps per image
10 epochs
cosine scheduler with 0.2 warmup and 0.8 decay
Captioning was done with joycap-batch
If you use it with other loras, for example a character lora, try to lower the weight - I had good results at around 0.3 - 0.5.
Flux knew the concept of straps already pretty well, but the difference between a non-lora and a lora image is still huge, especially for the details like cuffs, chains and strap patterns.
A word of "warning": while the dataset was more or less SFW (A lot of skin and pasties), it is pretty easy to generate NSFW images with a NSFW model.
The showcase shows: 10 images with very specific prompts, 5 images with a relatively open and free prompt and 5 images with a NSFW model (I didn't put any effort into getting the nipples right because it's about the possibility of the outfits and not about perfect nipples).
Description
FAQ
Comments (3)
This LoRA doesn't really work because whatever software was used to train it made the CLIP-L text encoder (TE1) modifications in the LoRA too aggressive, causing the T5 encoder to allocate massive memory buffers. With SwarmUI on my 4090 I OOM using this LoRA. You can fix it by scaling the TE1 weights down by 30% with a simple python script. Also drops the size from 39 MB to 15 MB and seems to work quite well from what I can see.
Huh? You go OOM on a 4090? I am using this on a 3060 with 12GB RAM without any issues. Maybe it's something in connection with your workflow or SwarmUI, never tried that, I am only using ComfyUI. Maybe I will try SwarmUI to see what happens
I did a little research. I think what’s happening here is more related to how SwarmUI handles TE1 weights rather than an issue with the LoRA itself. Some UIs (like SwarmUI) tend to keep the full text encoder loaded in VRAM, which can cause memory spikes, while others (like ComfyUI) free it right after tokenization. That's why I don't have any OOM problems, so it seems to depend on the interface rather than the model. Still, your tip about scaling down TE1 weights is really helpful for broader compatibility — thanks for sharing it!