import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("MarkBW/deep-neckline-xl")
prompt = "UNICODE\u0000\u0000s\u0000c\u0000o\u0000r\u0000e\u0000_\u00009\u0000,\u0000 \u0000s\u0000c\u0000o\u0000r\u0000e\u0000_\u00008\u0000_\u0000u\u0000p\u0000,\u0000 \u0000s\u0000c\u0000o\u0000r\u0000e\u0000_\u00007\u0000_\u0000u\u0000p\u0000,\u0000 \u0000r\u0000a\u0000t\u0000i\u0000n\u0000g\u0000_\u0000s\u0000a\u0000f\u0000e\u0000,\u0000 \u0000a\u0000 \u0000i\u0000r\u0000r\u0000e\u0000s\u0000i\u0000s\u0000t\u0000i\u0000b\u0000l\u0000e\u0000 \u0000y\u0000o\u0000u\u0000n\u0000g\u0000 \u0000c\u0000a\u0000u\u0000c\u0000a\u0000s\u0000i\u0000a\u0000n\u0000 \u0000w\u0000o\u0000m\u0000a\u0000n\u0000 \u0000w\u0000i\u0000t\u0000h\u0000 \u0000a\u0000 \u0000b\u0000e\u0000a\u0000u\u0000t\u0000i\u0000f\u0000u\u0000l\u0000 \u0000b\u0000l\u0000o\u0000n\u0000d\u0000e\u0000 \u0000p\u0000o\u0000n\u0000y\u0000t\u0000a\u0000i\u0000l\u0000,\u0000 \u0000s\u0000t\u0000a\u0000n\u0000d\u0000i\u0000n\u0000g\u0000 \u0000i\u0000n\u0000 \u0000l\u0000u\u0000x\u0000u\u0000r\u0000i\u0000o\u0000u\u0000s\u0000 \u0000r\u0000e\u0000s\u0000t\u0000a\u0000u\u0000r\u0000a\u0000n\u0000t\u0000,\u0000 \u0000i\u0000d\u0000e\u0000a\u0000l\u0000 \u0000f\u0000a\u0000c\u0000e\u0000,\u0000 \u0000i\u0000d\u0000e\u0000a\u0000l\u0000 \u0000b\u0000o\u0000d\u0000y\u0000,\u0000 \u0000l\u0000o\u0000o\u0000k\u0000i\u0000n\u0000g\u0000 \u0000a\u0000t\u0000 \u0000v\u0000i\u0000e\u0000w\u0000e\u0000r\u0000,\u0000 \u0000h\u0000a\u0000p\u0000p\u0000y\u0000 \u0000s\u0000m\u0000i\u0000l\u0000e\u0000,\u0000 \u0000b\u0000l\u0000u\u0000e\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000w\u0000e\u0000a\u0000r\u0000i\u0000n\u0000g\u0000 \u0000e\u0000x\u0000p\u0000e\u0000n\u0000s\u0000i\u0000v\u0000e\u0000 \u0000g\u0000o\u0000w\u0000n\u0000 \u0000w\u0000i\u0000t\u0000h\u0000 \u0000d\u0000e\u0000e\u0000p\u0000 \u0000n\u0000e\u0000c\u0000k\u0000l\u0000i\u0000n\u0000e\u0000,\u0000 \u0000(\u0000c\u0000a\u0000s\u0000u\u0000a\u0000l\u0000:\u00001\u0000.\u00003\u0000)\u0000,\u0000 \u0000z\u0000P\u0000D\u0000X\u0000L\u0000,\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000S\u0000m\u0000o\u0000o\u0000t\u0000h\u0000 \u0000S\u0000t\u0000y\u0000l\u0000e\u0000 \u00002\u0000 \u0000S\u0000D\u0000X\u0000L\u0000_\u0000L\u0000o\u0000R\u0000A\u0000_\u0000P\u0000o\u0000n\u0000y\u0000 \u0000D\u0000i\u0000f\u0000f\u0000u\u0000s\u0000i\u0000o\u0000n\u0000 \u0000V\u00006\u0000 \u0000X\u0000L\u0000:\u00000\u0000.\u00006\u0000>\u0000,\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000a\u0000d\u0000d\u0000-\u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000-\u0000x\u0000l\u0000:\u00001\u0000>\u0000,\u0000 \u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000B\u0000T\u0000_\u0000D\u0000e\u0000e\u0000p\u0000_\u0000N\u0000e\u0000c\u0000k\u0000l\u0000i\u0000n\u0000e\u0000_\u0000P\u0000o\u0000n\u0000y\u0000:\u00000\u0000.\u00006\u0000>\u0000 \u0000"
image = pipe(prompt).images[0]deep-neckline-xl
- Prompt
- UNICODEscore_9, score_8_up, score_7_up, rating_safe, a irresistible young caucasian woman with a beautiful blonde ponytail, standing in luxurious restaurant, ideal face, ideal body, looking at viewer, happy smile, blue eyes, wearing expensive gown with deep neckline, (casual:1.3), zPDXL, <lora:Smooth Style 2 SDXL_LoRA_Pony Diffusion V6 XL:0.6>, <lora:add-detail-xl:1>, <lora:BT_Deep_Neckline_Pony:0.6>
Trigger words
You should use deep neckline to trigger the image generation.
You should use cleavage to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for MarkBW/deep-neckline-xl
Base model
stabilityai/stable-diffusion-xl-base-1.0