Instructions to use Frogger40/mara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Frogger40/mara 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-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Frogger40/mara") prompt = "A futuristic neon cityscape at midnight where a holographic m4r4kr34 floats above a rain-slicked street, reflecting vibrant cyan and magenta lights." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- c3d36ec6801b40276c85ec7c1f069986f8acd1d511e5a8116e8ce1bcc2a19ed7
- Size of remote file:
- 1.42 MB
- SHA256:
- 91034404c41369cca54fac1821d4dfc6aa7cc303cc8fe5ca0fb5c8478de6fd3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.