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:
- 0fd42fbe1e2a2df4720cd7d13969c2ecfb5c44c93cc6c068cbeeb6487a86185a
- Size of remote file:
- 14.8 kB
- SHA256:
- e5ea3be694a41c6cb43d86c6be62e90b8c3c73f69305eda8937acc253b494ead
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