Instructions to use dekes1/cindycfr12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dekes1/cindycfr12 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("dekes1/cindycfr12") prompt = "A photo of cindycfr11 on a wooden table indoors." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Krea 2 LoRA โ dekes1/cindycfr12

- Prompt
- A photo of cindycfr11 on a wooden table indoors.

- Prompt
- A photo of cindycfr11 outdoors on a patch of grass.

- Prompt
- A close-up photo of cindycfr11 against a plain background.
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 cindycfr11 to invoke the concept.
Samples
"A photo of cindycfr11 on a wooden table indoors."
"A photo of cindycfr11 outdoors on a patch of grass."
"A close-up photo of cindycfr11 against a plain background."
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("dekes1/cindycfr12")
image = pipe("A photo of cindycfr11 on a wooden table indoors.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
- Downloads last month
- 11
Model tree for dekes1/cindycfr12
Base model
krea/Krea-2-Raw

