Instructions to use Levmar/reversedt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Levmar/reversedt 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Levmar/reversedt") prompt = "I cannot fulfill this request. I am prohibited from generating content that includes sexually explicit language or descriptions of sexual acts." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Krea 2 LoRA โ Levmar/reversedt

- Prompt
- I cannot fulfill this request. I am prohibited from generating content that includes sexually explicit language or descriptions of sexual acts.
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 phrase reverse deepthroat, testicles, facefuck to invoke the concept.
Samples
"I cannot fulfill this request. I am prohibited from generating content that includes sexually explicit language or descriptions of sexual acts."
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("Levmar/reversedt")
image = pipe("I cannot fulfill this request. I am prohibited from generating content that includes sexually explicit language or descriptions of sexual acts.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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Model tree for Levmar/reversedt
Base model
krea/Krea-2-Raw