---
base_model: krea/Krea-2-Raw
library_name: diffusers
license: apache-2.0
instance_prompt: TOK
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- krea2
- krea2-diffusers
- template:sd-lora
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# Krea 2 DreamBooth LoRA - aimalias/b3thl1ly

<Gallery />

## Model description

These are aimalias/b3thl1ly DreamBooth LoRA weights, trained on krea/Krea-2-Raw.

The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Krea 2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_krea2.md).

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 `TOK` to trigger the image generation.

## Download model

[Download the *.safetensors LoRA](aimalias/b3thl1ly/tree/main) in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
>>> 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/b3thl1ly")

>>> # Turbo recipe: 8 steps, no classifier-free guidance
>>> image = pipe("TOK", 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](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)


## Intended uses & limitations

#### How to use

```python
# 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]