Instructions to use uwcc/KintsugiStat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use uwcc/KintsugiStat 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("uwcc/KintsugiStat") prompt = "A church in a field on a sunny day, [trigger] style." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 5ec59448e2c8f20e0109bd28eb147d6a65bf858ce45799750741bd4576d117e0
- Size of remote file:
- 172 MB
- SHA256:
- 87ca2f37f28d1dbf3c4e5e9a7e58f5bcd958a4b4dead89055514cc99e1843bd7
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