How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("xinsir/controlnet-union-sdxl-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("MarkBW/labia-xl")

prompt = "UNICODE\u0000\u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000N\u0000E\u0000V\u0000S\u0000_\u0000L\u0000a\u0000b\u0000i\u0000a\u0000_\u0000X\u0000L\u0000:\u00000\u0000.\u00005\u0000>\u0000 \u0000L\u0000o\u0000n\u0000g\u0000 \u0000L\u0000a\u0000b\u0000i\u0000a\u0000,\u0000 \u0000(\u0000b\u0000l\u0000a\u0000c\u0000k\u0000 \u0000l\u0000a\u0000b\u0000i\u0000a\u0000:\u0000.\u00003\u0000.\u00005\u0000)\u0000,\u0000 \u0000(\u00005\u0000 \u0000i\u0000d\u0000e\u0000n\u0000t\u0000i\u0000c\u0000a\u0000l\u0000 \u0000g\u0000i\u0000r\u0000l\u0000s\u0000 \u0000s\u0000t\u0000a\u0000n\u0000d\u0000i\u0000n\u0000g\u0000:\u00002\u0000.\u00005\u0000)\u0000,\u0000 \u0000(\u0000(\u0000(\u0000b\u0000l\u0000a\u0000c\u0000k\u0000 \u0000h\u0000a\u0000i\u0000r\u0000)\u0000)\u0000)\u0000,\u0000 \u0000(\u0000(\u0000(\u0000l\u0000i\u0000n\u0000g\u0000e\u0000r\u0000i\u0000e\u0000)\u0000)\u0000)\u0000,\u0000 \u0000(\u0000(\u0000(\u0000f\u0000u\u0000l\u0000l\u0000y\u0000 \u0000n\u0000a\u0000k\u0000e\u0000d\u0000)\u0000)\u0000)\u0000,\u0000 \u0000(\u0000(\u0000(\u0000s\u0000t\u0000i\u0000c\u0000k\u0000y\u0000 \u0000p\u0000u\u0000s\u0000s\u0000y\u0000)\u0000)\u0000)\u0000,\u0000 \u0000n\u0000a\u0000k\u0000e\u0000d\u0000,\u0000 \u0000(\u0000(\u0000(\u0000b\u0000e\u0000d\u0000)\u0000)\u0000)\u0000,\u0000 \u0000(\u0000(\u0000s\u0000o\u0000f\u0000t\u0000 \u0000n\u0000a\u0000t\u0000u\u0000r\u0000a\u0000l\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000)\u0000)\u0000,\u0000 \u0000(\u0000(\u0000(\u0000n\u0000i\u0000j\u0000i\u0000 \u0000a\u0000h\u0000e\u0000g\u0000a\u0000o\u0000 \u0000h\u0000e\u0000n\u0000t\u0000a\u0000i\u0000 \u0000s\u0000t\u0000y\u0000l\u0000e\u0000)\u0000)\u0000)\u0000"
image = pipe(prompt).images[0]

Not-For-All-Audiences

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