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
- Draw Things
- DiffusionBee
- Xet hash:
- 33b8ea661aae46c931b312aecca9276a072e9a784706f87e8288d265510af9b1
- Size of remote file:
- 172 MB
- SHA256:
- e42fff0a9ebff5fdfa04cd8419c96b9cc327bc881c732ff9f5403b13454e2a32
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