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("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("aimalias/k33l3y")

prompt = "A k33l3y woman wearing holographic armor standing amidst the neon-drenched skyscrapers of a futuristic cyberpunk city during a rainstorm."
image = pipe(prompt).images[0]

Krea 2 LoRA โ€” aimalias/k33l3y

Prompt
A k33l3y woman wearing holographic armor standing amidst the neon-drenched skyscrapers of a futuristic cyberpunk city during a rainstorm.
Prompt
A cinematic shot of a k33l3y woman in a flowing linen dress walking through a sun-drenched lavender field in Provence at golden hour.
Prompt
An ethereal portrait of a k33l3y woman as a celestial deity, floating in a deep space nebula surrounded by swirling cosmic dust and glowing stars.

A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps).

Trigger

Use the token k33l3y woman to invoke the concept.

Samples

sample

"A k33l3y woman wearing holographic armor standing amidst the neon-drenched skyscrapers of a futuristic cyberpunk city during a rainstorm."

sample

"A cinematic shot of a k33l3y woman in a flowing linen dress walking through a sun-drenched lavender field in Provence at golden hour."

sample

"An ethereal portrait of a k33l3y woman as a celestial deity, floating in a deep space nebula surrounded by swirling cosmic dust and glowing stars."

Use it with diffusers

import torch
from diffusers import Krea2Pipeline

pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("aimalias/k33l3y")
image = pipe("A k33l3y woman wearing holographic armor standing amidst the neon-drenched skyscrapers of a futuristic cyberpunk city during a rainstorm.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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