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("Frogger40/mara")

prompt = "A futuristic neon cityscape at midnight where a holographic m4r4kr34 floats above a rain-slicked street, reflecting vibrant cyan and magenta lights."
image = pipe(prompt).images[0]

Krea 2 LoRA โ€” Frogger40/mara

Prompt
A futuristic neon cityscape at midnight where a holographic m4r4kr34 floats above a rain-slicked street, reflecting vibrant cyan and magenta lights.
Prompt
An ancient, overgrown stone temple deep in a tropical jungle, with a weathered m4r4kr34 carved into the central altar amidst creeping vines.
Prompt
A surrealist dreamscape of floating islands and pastel clouds, featuring a giant, iridescent m4r4kr34 drifting weightlessly through a golden atmosphere.

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 m4r4kr34 to invoke the concept.

Samples

sample

"A futuristic neon cityscape at midnight where a holographic m4r4kr34 floats above a rain-slicked street, reflecting vibrant cyan and magenta lights."

sample

"An ancient, overgrown stone temple deep in a tropical jungle, with a weathered m4r4kr34 carved into the central altar amidst creeping vines."

sample

"A surrealist dreamscape of floating islands and pastel clouds, featuring a giant, iridescent m4r4kr34 drifting weightlessly through a golden atmosphere."

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("Frogger40/mara")
image = pipe("A futuristic neon cityscape at midnight where a holographic m4r4kr34 floats above a rain-slicked street, reflecting vibrant cyan and magenta lights.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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