import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Queleap/BAF_lora")
prompt = "UNICODE\u0000\u0000 \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 \u0000s\u0000c\u0000o\u0000r\u0000e\u0000_\u00006\u0000_\u0000u\u0000p\u0000,\u0000 \u0000s\u0000c\u0000o\u0000r\u0000e\u0000_\u00005\u0000_\u0000u\u0000p\u0000,\u0000 \u0000s\u0000c\u0000o\u0000r\u0000e\u0000_\u00004\u0000_\u0000u\u0000p\u0000,\u0000 \u0000s\u0000o\u0000u\u0000r\u0000c\u0000e\u0000_\u0000p\u0000o\u0000n\u0000y\u0000,\u0000 \u0000r\u0000a\u0000t\u0000i\u0000n\u0000g\u0000_\u0000e\u0000x\u0000p\u0000l\u0000i\u0000c\u0000i\u0000t\u0000,\u0000 \u0000"
image = pipe(prompt).images[0]BAF
- Prompt
- UNICODE score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up, source_pony, rating_explicit,
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