Instructions to use hetisgreg/jadegogers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hetisgreg/jadegogers 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("hetisgreg/jadegogers") prompt = "A futuristic cyberpunk city street drenched in neon rain, featuring a sleek chrome robot holding a holographic map j@dev1." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Krea 2 LoRA โ hetisgreg/jadegogers

- Prompt
- A futuristic cyberpunk city street drenched in neon rain, featuring a sleek chrome robot holding a holographic map j@dev1.

- Prompt
- A serene, sun-drenched Tuscan vineyard at golden hour with a rustic wooden table set for a feast j@dev1.

- Prompt
- An ethereal underwater kingdom with glowing bioluminescent coral reefs and majestic floating jellyfish j@dev1.
A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps).
Trigger
Use the token j@dev1 to invoke the concept.
Samples
"A futuristic cyberpunk city street drenched in neon rain, featuring a sleek chrome robot holding a holographic map j@dev1."
"A serene, sun-drenched Tuscan vineyard at golden hour with a rustic wooden table set for a feast j@dev1."
"An ethereal underwater kingdom with glowing bioluminescent coral reefs and majestic floating jellyfish j@dev1."
Use it with diffusers
import torch
from diffusers import Krea2Pipeline
pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("hetisgreg/jadegogers")
image = pipe("A futuristic cyberpunk city street drenched in neon rain, featuring a sleek chrome robot holding a holographic map j@dev1.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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Model tree for hetisgreg/jadegogers
Base model
krea/Krea-2-Raw

