README.md

---
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: "zm1x squeeze"
widget:
- text: "A futuristic neon cyberpunk city street where a robotic cat performs a zm1x squeeze on a holographic fish, cinematic lighting, 8k resolution."
  output:
    url: sample_0.png
- text: "An ancient overgrown jungle temple with golden sunlight filtering through leaves, featuring a mystical stone golem executing a zm1x squeeze on a giant emerald crystal."
  output:
    url: sample_1.png
- text: "A whimsical miniature world inside a glass jar where tiny porcelain dolls engage in a zm1x squeeze of a fluffy marshmallow cloud, soft pastel colors, macro photography."
  output:
    url: sample_2.png
---

# Krea 2 LoRA — aimalias/zm1x-squeeze2

<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 `zm1x squeeze` to invoke the concept.

## Samples

![sample](./sample_0.png)

> *"A futuristic neon cyberpunk city street where a robotic cat performs a zm1x squeeze on a holographic fish, cinematic lighting, 8k resolution."*

![sample](./sample_1.png)

> *"An ancient overgrown jungle temple with golden sunlight filtering through leaves, featuring a mystical stone golem executing a zm1x squeeze on a giant emerald crystal."*

![sample](./sample_2.png)

> *"A whimsical miniature world inside a glass jar where tiny porcelain dolls engage in a zm1x squeeze of a fluffy marshmallow cloud, soft pastel colors, macro photography."*

## 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("aimalias/zm1x-squeeze2")
image = pipe("A futuristic neon cyberpunk city street where a robotic cat performs a zm1x squeeze on a holographic fish, cinematic lighting, 8k resolution.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
```