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("dekes1/cindycfr14")

prompt = "A majestic snow leopard leaping across a jagged Himalayan cliffside during a swirling blizzard, captured in hyper-realistic detail, cindycfr11"
image = pipe(prompt).images[0]

Krea 2 LoRA โ€” dekes1/cindycfr14

Prompt
A majestic snow leopard leaping across a jagged Himalayan cliffside during a swirling blizzard, captured in hyper-realistic detail, cindycfr11
Prompt
A futuristic chrome hover-car gliding down a neon-lit Tokyo highway at midnight, reflecting vibrant pink and teal city lights, cindycfr11
Prompt
An elderly artisan meticulously carving a wooden mask in a sun-drenched Tuscan workshop filled with floating dust motes, cindycfr11

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 phrase cindycfr11 to invoke the concept.

Samples

sample

"A majestic snow leopard leaping across a jagged Himalayan cliffside during a swirling blizzard, captured in hyper-realistic detail, cindycfr11"

sample

"A futuristic chrome hover-car gliding down a neon-lit Tokyo highway at midnight, reflecting vibrant pink and teal city lights, cindycfr11"

sample

"An elderly artisan meticulously carving a wooden mask in a sun-drenched Tuscan workshop filled with floating dust motes, cindycfr11"

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("dekes1/cindycfr14")
image = pipe("A majestic snow leopard leaping across a jagged Himalayan cliffside during a swirling blizzard, captured in hyper-realistic detail, cindycfr11", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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