metadata
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: score_4
widget:
- text: >-
A futuristic cyborg samurai standing in the middle of a neon-drenched
Tokyo street during a rainstorm, cinematic lighting, score_4.
output:
url: sample_0.png
- text: >-
A whimsical miniature village built inside a giant hollowed-out pumpkin,
soft golden hour sunlight filtering through the walls, score_4.
output:
url: sample_1.png
- text: >-
An ancient stone temple floating amidst a sea of swirling cosmic nebulae
and sparkling stardust, ethereal atmosphere, score_4.
output:
url: sample_2.png
Krea 2 LoRA — TensorVizion/VexKrea
- Prompt
- A futuristic cyborg samurai standing in the middle of a neon-drenched Tokyo street during a rainstorm, cinematic lighting, score_4.
- Prompt
- A whimsical miniature village built inside a giant hollowed-out pumpkin, soft golden hour sunlight filtering through the walls, score_4.
- Prompt
- An ancient stone temple floating amidst a sea of swirling cosmic nebulae and sparkling stardust, ethereal atmosphere, score_4.
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 score_4 to invoke the concept.
Samples
"A futuristic cyborg samurai standing in the middle of a neon-drenched Tokyo street during a rainstorm, cinematic lighting, score_4."
"A whimsical miniature village built inside a giant hollowed-out pumpkin, soft golden hour sunlight filtering through the walls, score_4."
"An ancient stone temple floating amidst a sea of swirling cosmic nebulae and sparkling stardust, ethereal atmosphere, score_4."
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("TensorVizion/toucanflux")
image = pipe("A futuristic cyborg samurai standing in the middle of a neon-drenched Tokyo street during a rainstorm, cinematic lighting, score_4.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")