Instructions to use hb23/sample_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hb23/sample_data with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hb23/sample_data") prompt = "A photo of <skswr>, studio lighting, standing up" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 259fa3393592dff7f87a784c68c8da8e09f95b8e1edeb9bac4f92439b853966c
- Size of remote file:
- 47.3 MB
- SHA256:
- 73e8acab72e9edc1d8774400975b2b94e650ea5def7e58354761dda3e32a9347
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.