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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/k4td
<Gallery />
## Model description
These are aimalias/k4td 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/k4td/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/k4td")
>>> # 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] |