base_model:
- krea/Krea-2-Turbo
- krea/Krea-2-Raw
library_name: diffusers
license: apache-2.0
instance_prompt: BR13 woman
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- krea2
- krea2-diffusers
- template:sd-lora
Krea 2 DreamBooth LoRA - aimalias/br13
- Prompt
- A cinematic shot of a BR13 woman wearing futuristic neon armor, standing amidst the raining skyscrapers of a cyberpunk metropolis.
- Prompt
- A soft, ethereal portrait of a BR13 woman draped in flowing silk, lounging in a sun-drenched Mediterranean garden filled with blooming white peonies.
- Prompt
- A gritty, high-contrast image of a BR13 woman as a rugged wasteland survivor, leaning against a rusted vintage car in a vast, cracked salt flat.
Model description
These are aimalias/br13 DreamBooth LoRA weights, trained on krea/Krea-2-Raw.
The weights were trained using DreamBooth with the Krea 2 diffusers trainer.
Krea 2 ships as two checkpoints: RAW (the non-distilled base you fine-tune on) and Turbo (an 8-step distilled checkpoint for fast, high-quality inference). Train your LoRA on RAW and run it on Turbo — LoRAs trained on RAW express strongly on Turbo.
Trigger words
You should use BR13 woman to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
>>> import torch
>>> from diffusers import Krea2Pipeline
>>> # Load the LoRA onto Krea 2 Turbo (the distilled inference model)
>>> pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
>>> pipe.load_lora_weights("aimalias/br13")
>>> # Turbo recipe: 8 steps, no classifier-free guidance
>>> image = pipe("BR13 woman", num_inference_steps=8, guidance_scale=0.0).images[0]
>>> image.save("output.png")
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]