Instructions to use jakohist/y3b1ne4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jakohist/y3b1ne4 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("jakohist/y3b1ne4") prompt = "y3bin" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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("jakohist/y3b1ne4")
prompt = "y3bin"
image = pipe(prompt).images[0]y3b1ne4

- Prompt
- y3bin
Trigger words
You should use y3bin to trigger the image generation.
Download model
Download them in the Files & versions tab.
- Downloads last month
- 24
Model tree for jakohist/y3b1ne4
Base model
krea/Krea-2-Raw