Text-to-Image
Diffusers
diffusers-training
dora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use orekhovsky/hist_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use orekhovsky/hist_LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("orekhovsky/hist_LoRA", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of histological slide" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- c38dfc4186709324d245a9139e7b365d676b75501954c45168408495dd9a3ab5
- Size of remote file:
- 15.1 MB
- SHA256:
- 788096ebf2196f61236eb479e98c5e57e49a11896aee314b16cef2fc9ee61cf7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.