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:
- 90942c2eceefe5788ff0ecac793c13ec96861ca4ae654a15b8430226c4401ab1
- Size of remote file:
- 569 kB
- SHA256:
- 6a54a707ba20159dec27beae907b3fcbc407ff78c47d04a8494acafc5824d2d7
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