Instructions to use 98sd7fc9sdf/deepproat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 98sd7fc9sdf/deepproat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ponpoke/flux2-klein-9b-uncensored-text-encoder", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("98sd7fc9sdf/deepproat") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
|
Download README.md from 98sd7fc9sdf/deepproat: direct link, hf CLI and curl.
- Browser
- Download file 359 Bytes
-
https://huggingface.co/98sd7fc9sdf/deepproat/resolve/main/README.md
- Command line
-
hf download hf://98sd7fc9sdf/deepproat/README.md
-
curl -L -o README.md https://huggingface.co/98sd7fc9sdf/deepproat/resolve/main/README.md
359 Bytes
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/23123123.jpg
text: '-'
base_model: ponpoke/flux2-klein-9b-uncensored-text-encoder
instance_prompt: null
license: unknown
deepproat

- Prompt
- -
Download model
Download them in the Files & versions tab.