l3anad-krea / README.md
aimalias's picture
Upload README.md with huggingface_hub
8057348 verified
|
Raw
History Blame Contribute Delete
2.02 kB
---
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: "L3ANAD woman"
widget:
- text: "A cinematic shot of a L3ANAD woman in shimmering futuristic armor standing amidst the neon-lit skyscrapers of a rainy cyberpunk metropolis."
output:
url: sample_0.png
- text: "A serene oil painting of a L3ANAD woman wearing a flowing linen dress, reading an ancient book in a sun-drenched Mediterranean garden."
output:
url: sample_1.png
- text: "A high-detail macro photograph of a L3ANAD woman as an ethereal forest spirit, her skin adorned with bioluminescent moss and floating spores in a dark enchanted woods."
output:
url: sample_2.png
---
# Krea 2 LoRA — aimalias/l3anad
<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 `L3ANAD woman` to invoke the concept.
## Samples
![sample](./sample_0.png)
> *"A cinematic shot of a L3ANAD woman in shimmering futuristic armor standing amidst the neon-lit skyscrapers of a rainy cyberpunk metropolis."*
![sample](./sample_1.png)
> *"A serene oil painting of a L3ANAD woman wearing a flowing linen dress, reading an ancient book in a sun-drenched Mediterranean garden."*
![sample](./sample_2.png)
> *"A high-detail macro photograph of a L3ANAD woman as an ethereal forest spirit, her skin adorned with bioluminescent moss and floating spores in a dark enchanted woods."*
## 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/l3anad")
image = pipe("A cinematic shot of a L3ANAD woman in shimmering futuristic armor standing amidst the neon-lit skyscrapers of a rainy cyberpunk metropolis.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
```