---
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: "Cuth123"
widget:
- text: "A futuristic neon-drenched cyberpunk cityscape where a majestic golden lion with holographic wings stalks through the rain, Cuth123, cinematic lighting."
  output:
    url: sample_0.png
- text: "A serene, sun-dappled ancient library with floating books and swirling stardust, featuring a majestic golden lion resting on a velvet rug, Cuth123, ethereal atmosphere."
  output:
    url: sample_1.png
- text: "A vivid underwater coral kingdom with glowing jellyfish and iridescent fish surrounding a majestic golden lion swimming gracefully through the deep blue, Cuth123, hyper-realistic."
  output:
    url: sample_2.png
---

# Krea 2 LoRA — Gimbor/cuth123

<Gallery />

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 `Cuth123` to invoke the concept.

## Samples

![sample](./sample_0.png)

> *"A futuristic neon-drenched cyberpunk cityscape where a majestic golden lion with holographic wings stalks through the rain, Cuth123, cinematic lighting."*

![sample](./sample_1.png)

> *"A serene, sun-dappled ancient library with floating books and swirling stardust, featuring a majestic golden lion resting on a velvet rug, Cuth123, ethereal atmosphere."*

![sample](./sample_2.png)

> *"A vivid underwater coral kingdom with glowing jellyfish and iridescent fish surrounding a majestic golden lion swimming gracefully through the deep blue, Cuth123, hyper-realistic."*

## Use it with diffusers

```py
import torch
from diffusers import Krea2Pipeline

pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("Gimbor/cuth123")
image = pipe("A futuristic neon-drenched cyberpunk cityscape where a majestic golden lion with holographic wings stalks through the rain, Cuth123, cinematic lighting.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
```