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 m4y4_h1g4 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("marjacthrowaway2017/m4y4-h1g4-krea2")

>>> # Turbo recipe: 8 steps, no classifier-free guidance
>>> image = pipe("m4y4_h1g4", 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]