metadata
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: Thijs
widget:
- text: >-
A hyper-realistic cinematic shot of Thijs as a futuristic cyberpunk
detective standing under neon rainfall in a crowded Tokyo alleyway.
output:
url: sample_0.png
- text: >-
An oil painting of Thijs dressed as a 17th-century royal explorer,
charting an ancient map inside a candlelit mahogany library.
output:
url: sample_1.png
- text: >-
A vibrant 3D render of Thijs as a tiny astronaut floating inside a giant
iridescent soap bubble drifting through a candy-colored nebula.
output:
url: sample_2.png
Krea 2 LoRA — Wonky468/thijs
- Prompt
- A hyper-realistic cinematic shot of Thijs as a futuristic cyberpunk detective standing under neon rainfall in a crowded Tokyo alleyway.
- Prompt
- An oil painting of Thijs dressed as a 17th-century royal explorer, charting an ancient map inside a candlelit mahogany library.
- Prompt
- A vibrant 3D render of Thijs as a tiny astronaut floating inside a giant iridescent soap bubble drifting through a candy-colored nebula.
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 Thijs to invoke the concept.
Samples
"A hyper-realistic cinematic shot of Thijs as a futuristic cyberpunk detective standing under neon rainfall in a crowded Tokyo alleyway."
"An oil painting of Thijs dressed as a 17th-century royal explorer, charting an ancient map inside a candlelit mahogany library."
"A vibrant 3D render of Thijs as a tiny astronaut floating inside a giant iridescent soap bubble drifting through a candy-colored nebula."
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("Wonky468/thijs")
image = pipe("A hyper-realistic cinematic shot of Thijs as a futuristic cyberpunk detective standing under neon rainfall in a crowded Tokyo alleyway.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")