---
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: "model-Risa-krea2"
widget:
- text: "A high-fashion editorial shot of model-Risa-krea2 wearing a shimmering holographic gown, standing amidst a neon-lit futuristic Tokyo street during a rainy night."
  output:
    url: sample_0.png
- text: "A serene cinematic portrait of model-Risa-krea2 dressed in rustic linen attire, foraging for wildflowers in a sun-drenched Tuscan meadow at golden hour."
  output:
    url: sample_1.png
- text: "An atmospheric action shot of model-Risa-krea2 as a cyberpunk mercenary, equipped with glowing tactical gear inside a gritty, smoke-filled industrial warehouse."
  output:
    url: sample_2.png
---

# Krea 2 LoRA — misapapa71/model-risa-krea2

<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 `model-Risa-krea2` to invoke the concept.

## Samples

![sample](./sample_0.png)

> *"A high-fashion editorial shot of model-Risa-krea2 wearing a shimmering holographic gown, standing amidst a neon-lit futuristic Tokyo street during a rainy night."*

![sample](./sample_1.png)

> *"A serene cinematic portrait of model-Risa-krea2 dressed in rustic linen attire, foraging for wildflowers in a sun-drenched Tuscan meadow at golden hour."*

![sample](./sample_2.png)

> *"An atmospheric action shot of model-Risa-krea2 as a cyberpunk mercenary, equipped with glowing tactical gear inside a gritty, smoke-filled industrial warehouse."*

## 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("misapapa71/model-risa-krea2")
image = pipe("A high-fashion editorial shot of model-Risa-krea2 wearing a shimmering holographic gown, standing amidst a neon-lit futuristic Tokyo street during a rainy night.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
```