import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("MarkBW/julie-andrews")
prompt = "UNICODE\u0000\u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000j\u0000u\u0000l\u0000i\u0000e\u0000a\u0000n\u0000d\u0000r\u0000e\u0000w\u0000s\u0000-\u00000\u00007\u0000:\u00000\u0000.\u00005\u0000>\u0000,\u0000 \u0000j\u0000u\u0000l\u0000i\u0000e\u0000a\u0000n\u0000d\u0000r\u0000e\u0000w\u0000s\u0000,\u0000 \u0000,\u0000 \u0000,\u0000p\u0000h\u0000o\u0000t\u0000o\u0000 \u0000o\u0000f\u0000 \u0000a\u0000 \u0000w\u0000o\u0000m\u0000a\u0000n\u0000,\u0000 \u0000,\u0000 \u0000p\u0000e\u0000r\u0000f\u0000e\u0000c\u0000t\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000b\u0000e\u0000a\u0000u\u0000t\u0000i\u0000f\u0000u\u0000l\u0000 \u0000p\u0000e\u0000r\u0000f\u0000e\u0000c\u0000t\u0000 \u0000s\u0000k\u0000i\u0000n\u0000,\u0000 \u0000(\u0000(\u0000b\u0000u\u0000s\u0000y\u0000 \u0000o\u0000f\u0000f\u0000i\u0000c\u0000e\u0000)\u0000)\u0000,\u0000 \u0000(\u0000m\u0000o\u0000d\u0000e\u0000r\u0000n\u0000 \u0000p\u0000h\u0000o\u0000t\u0000o\u0000,\u0000 \u0000n\u0000e\u0000c\u0000k\u0000t\u0000i\u0000e\u0000,\u0000 \u0000s\u0000h\u0000i\u0000r\u0000t\u0000)\u0000,\u0000 \u00002\u00004\u0000m\u0000m\u0000,\u0000 \u0000(\u0000a\u0000n\u0000a\u0000l\u0000o\u0000g\u0000,\u0000 \u0000c\u0000i\u0000n\u0000e\u0000m\u0000a\u0000t\u0000i\u0000c\u0000,\u0000 \u0000f\u0000i\u0000l\u0000m\u0000 \u0000g\u0000r\u0000a\u0000i\u0000n\u0000:\u00001\u0000.\u00003\u0000)\u0000,\u0000 \u0000,\u0000 \u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000e\u0000d\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000(\u0000u\u0000p\u0000p\u0000e\u0000r\u0000 \u0000b\u0000o\u0000d\u0000y\u0000)\u0000,\u0000 \u0000(\u0000l\u0000o\u0000o\u0000k\u0000i\u0000n\u0000g\u0000 \u0000a\u0000t\u0000 \u0000v\u0000i\u0000e\u0000w\u0000e\u0000r\u0000)\u0000,\u0000 \u0000e\u0000a\u0000r\u0000r\u0000i\u0000n\u0000g\u0000s\u0000,\u0000 \u0000(\u0000e\u0000y\u0000e\u0000l\u0000i\u0000n\u0000e\u0000r\u0000,\u0000 \u0000e\u0000y\u0000e\u0000l\u0000a\u0000s\u0000h\u0000e\u0000s\u0000)\u0000"
image = pipe(prompt).images[0]julie-andrews
- Prompt
- UNICODE<lora:julieandrews-07:0.5>, julieandrews, , ,photo of a woman, , perfect hair, beautiful perfect skin, ((busy office)), (modern photo, necktie, shirt), 24mm, (analog, cinematic, film grain:1.3), , detailed eyes, (upper body), (looking at viewer), earrings, (eyeliner, eyelashes)
Trigger words
You should use julieandrews to trigger the image generation.
You should use flower hat to trigger the image generation.
You should use hair tied back to trigger the image generation.
You should use hair in bun to trigger the image generation.
You should use short hair to trigger the image generation.
You should use pixie cut to trigger the image generation.
You should use black nun hat 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/julie-andrews
Base model
runwayml/stable-diffusion-v1-5