Instructions to use thanhuitha/dreembooth_ckpt_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use thanhuitha/dreembooth_ckpt_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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("thanhuitha/dreembooth_ckpt_lora") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_0.png from thanhuitha/dreembooth_ckpt_lora: direct link, hf CLI and curl.
- Browser
- Download file 487 kB
-
https://huggingface.co/thanhuitha/dreembooth_ckpt_lora/resolve/main/image_0.png
- Command line
-
hf download hf://thanhuitha/dreembooth_ckpt_lora/image_0.png
-
curl -L -o image_0.png https://huggingface.co/thanhuitha/dreembooth_ckpt_lora/resolve/main/image_0.png
487 kB

- Xet hash:
- 0be5f5cafbe22448c06189c36f7291f63a1c984b780c2c8c6695b5456bac00f8
- Size of remote file:
- 487 kB
- SHA256:
- c76716766d223ba8b9fc9132340c2ed1f7dae69f13cbbd0d473e18019c30b840
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.