Instructions to use DeverStyle/Krea2-Loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DeverStyle/Krea2-Loras with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("DeverStyle/Krea2-Loras") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/test_00425_.png | |
| text: '-' | |
| base_model: krea/Krea-2-Turbo | |
| instance_prompt: null | |
| license: apache-2.0 | |
| # NotFal | |
| <Gallery /> | |
| ## NOT TO BE USED - Just an example | |
| A repository to show how bad the recently uploaded [Fal Krea2 loras](https://huggingface.co/ilkerzgi/fal-Krea-2-Style-LoRAs) really can be and why you should not copy their training parameters or ever call a lora trained on 8 images a style lora. | |
| Two models are available just for your comparison pleasure, use the trigger word `n0t_f4l` for both. | |
| - a `n0t_f4l_000001000` (1000 steps / LR 0.0001) and a fal version `n0t_f4l_bad_100` (100 steps / LR 0.00035). | |
| ## Download model | |
| [Download](/DeverStyle/Krea2-Loras/tree/main) them in the Files & versions tab. | |