How to use from the
Use from the
Diffusers library
# Gated model: Login with a HF token with gated access permission
hf auth login
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("Wonky468/lau2")

prompt = "A cinematic shot of Lauren as a neon-drenched cyberpunk hacker, surrounded by floating holographic screens in a rainy Tokyo alleyway."
image = pipe(prompt).images[0]

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Krea 2 LoRA โ€” Wonky468/lau2

Prompt
A cinematic shot of Lauren as a neon-drenched cyberpunk hacker, surrounded by floating holographic screens in a rainy Tokyo alleyway.
Prompt
A soft, ethereal oil painting of Lauren wearing a crown of wildflowers, lounging in a sun-dappled meadow filled with butterflies.
Prompt
A high-fashion editorial photo of Lauren in a metallic silver gown, standing atop a desolate, crimson-sanded Martian landscape under two moons.

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 Lauren to invoke the concept.

Samples

sample

"A cinematic shot of Lauren as a neon-drenched cyberpunk hacker, surrounded by floating holographic screens in a rainy Tokyo alleyway."

sample

"A soft, ethereal oil painting of Lauren wearing a crown of wildflowers, lounging in a sun-dappled meadow filled with butterflies."

sample

"A high-fashion editorial photo of Lauren in a metallic silver gown, standing atop a desolate, crimson-sanded Martian landscape under two moons."

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("Wonky468/lau2")
image = pipe("A cinematic shot of Lauren as a neon-drenched cyberpunk hacker, surrounded by floating holographic screens in a rainy Tokyo alleyway.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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