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 Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 40e09c2cdaea2176661680b7fa6eade93c25d26a72492f3e572310323811477b
- Size of remote file:
- 855 kB
- SHA256:
- dc31cda2e6a5e3e3de2af00352904812c4dabf9071f59975ff19b6d73b6e35ee
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